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Supercomputer-based advanced ladar imaging simulator (ALIS)
The Advanced Ladar Imaging Simulator (ALIS) is a comprehensive multi-dimensional laser radar system simulator that models complex atmospheric scenes combined with high-resolution solid object scenes. The primary functions of ALIS are to serve as a laser radar sensor design tool, data product generator for exploitation, and a decision aid for implementing system designs. This paper focuses on the software structure of the simulator and the challenges that it presents. The ambient atmospheric scene generation is augmented with built-in approximate plume models or with external large-scale Navier-Stokes computational fluid dynamics plume models. The mixed atmosphere and solid object scene is generated via an adaptively meshed, over-sampled voxel representation predicated jointly on the sensor capabilities and scene complexity. To our knowledge, ALIS goes beyond previous ladar simulators with detailed atmospheric turbulence effects and time-dependent plume dispersion models for direct and coherent detection frequency-agile transceivers. ALIS models a wide range of ladar architectures, treating laser coherence properties, receiver electronics noise/transfer functions, and electronics/photon statistical noise. It provides complex amplitude ladar echo "range cubes" (all range reports along a given line-of-sight) for the composite atmosphere-solid scene. The model complexity and its capability to process large (>109) voxel count scenes is accommodated with a portable, scalable software architecture that supports single processors to fine-grained parallel supercomputers.
Smith, Duane D.; Nichols, Terry L.; Gatt, Philip; Lee, Kotik K.; Sicking, Charles; Seida, Steven B.; Coker, Charles F.; Perry, Kimberly M.; Coker, Jason S.
Geospatial analysis based on GIS integrated with LADAR.
In this work, we describe multi-layered analyses of a high-resolution broad-area LADAR data set in support of expeditionary activities. High-level features are extracted from the LADAR data, such as the presence and location of buildings and cars, and then these features are used to populate a GIS (geographic information system) tool. We also apply line-of-sight (LOS) analysis to develop a path-planning module. Finally, visualization is addressed and enhanced with a gesture-based control system that allows the user to navigate through the enhanced data set in a virtual immersive experience. This work has operational applications including military, security, disaster relief, and task-based robotic path planning. PMID:24104270
Fetterman, Matt R; Freking, Robert; Fernandez-Cull, Christy; Hinkle, Christopher W; Myne, Anu; Relyea, Steven; Winslow, Jim
Imaging through obscurants with a heterodyne detection-based ladar system
Bridger Photonics has been researching and developing a ladar system based on heterodyne detection for imaging through brownout and other DVEs. There are several advantages that an FMCW ladar system provides compared to direct detect pulsed time-of-flight systems including: 1) Higher average powers, 2) Single photon sensitive while remaining tolerant to strong return signals, 3) Doppler sensitivity for clutter removal, and 4) More flexible system for sensing during various stages of flight. In this paper, we provide a review of our sensor, discuss lessons learned during various DVE tests, and show our latest 3D imagery.
Reibel, Randy R.; Roos, Peter A.; Kaylor, Brant M.; Berg, Trenton J.; Curry, James R.
The synthetic aperture imaging ladar (SAIL) systems typically generate large amounts of data difficult to compress with digital method. This paper presents an optical SAIL processor based on compensation of quadratic phase of echo in azimuth direction and two dimensional Fourier transform. The optical processor mainly consists of one phase-only liquid crystal spatial modulator(LCSLM) to load the phase data of target echo and one cylindrical lens to compensate the quadratic phase and one spherical lens to fulfill the task of two dimensional Fourier transform. We show the imaging processing result of practical target echo obtained by a synthetic aperture imaging ladar demonstrator. The optical processor is compact and lightweight and could provide inherent parallel and the speed-of-light computing capability, it has a promising application future especially in onboard and satellite borne SAIL systems.
Sun, Zhiwei; Zhi, Ya'nan; Liu, Liren; Sun, Jianfeng; Zhou, Yu; Hou, Peipei
Performance improvement of real-time 3D imaging ladar based on a modified array receiver
In the previous study, we have demonstrated the first development result of the 3D imaging LADAR (LAser Detection And Ranging) which can obtain the 3D data using linear array receiver. The system consists of in-house-made key components. The linear array receiver consists of the previously reported APD (Avalanche Photo Diode) array, the ROIC (Read Out Integrated Circuit) array assembled in one package, and the transmitting optics using pupil divide method which realizes a uniform illumination on a target. In this paper, we report the advanced 3D imaging LADAR with improved ROIC. The ROIC has the function to set the optimum threshold for pulse peak detection in each element and switch the measurement range width on a case by case basis. Moreover, the response of MUX in ROIC is improved. Installing this ROIC, we realized 256× 256 pixels range imaging with an on-line frame rate of more than 30 Hz. Then, we tried online object detection with the obtained 3D image using a simple detection algorithm. We demonstrated system has the potential to detect the object even in the scene with some clutters.
Kotake, Nobuki; Hirai, Akihito; Kameyama, Shumpei; Imaki, Masaharu; Tsuji, Hidenobu; Takabayashi, Mikio; Sasahata, Yoshifumi; Hirano, Yoshihito
LADAR Proximity Fuze - System Study -
Digital Repository Infrastructure Vision for European Research (DRIVER)
LADAR (Laser Detection and Ranging) systems constitue a direct extension of the conventional radar techniques. Because they operate at much shorter wavelengths, LADARs have the unique capability to generate 3D images of objects. These laser systems have many applications in both the civilian and the defence fields concerning target detection and identification. The extraction of these features depends on the processing algorithms, target properties and 3D images quality. In order to support f...
Blanquer, Eric
Pose recognition of articulated target based on ladar range image with elastic shape analysis
Elastic shape analysis is introduced for pose recognition of articulated target which is based on small samples of ladar range images. Shape deformations caused by poses changes represented as closed elastic curves given by the square-root velocity function geodesics are used to quantify shape differences and the Karcher mean is used to build a model library. Three kinds of moments - Hu moment invariants, affine moment invariants, and Zernike moment invariants based on support vector machines (SVMs) - are applied to evaluate this approach. The experiment results show that no matter what the azimuth angles of the testing samples are, this approach is capable of achieving a high recognition rate using only 3 model samples with different carrier to noise ratios (CNR); the performance of this approach is much better than that of three kinds of moments based on SVM, especially under high noise conditions.
Liu, Zheng-Jun; Li, Qi; Wang, Qi
This paper presents an improved system structure of photon-counting chirped amplitude modulation (AM) ladar based on the Geiger-mode avalanche photoelectric detector (GmAPD). The error-pulse probability is investigated with statistical method. The research shows that most of the error pulses that are triggered by noise are distributed in the intensity troughs of the chirped AM waveform. The error-pulse probability is lowered with the sliding window and the threshold. With the average intensity of noise and signal being 0.3 count/sample and 1 count/sample, respectively, the probability of error pulses is reduced from 12% to 1.0%, and the SNR is improved by 2.2 dB in the improved system. PMID:22193131
Zhang, Zijing; Wu, Long; Zhang, Yu; Zhao, Yuan; Sun, Xiudong
A two-dimensional (2D) Fourier transform algorithm for the image reconstruction of synthetic-aperture imaging ladar (SAIL) collected data is suggested. This algorithm consists of quadratic phase compensation in azimuth direction and 2D fast Fourier transform. Based on this algorithm and the parallel 2D Fourier transform capability of spherical lens, an optical principle scheme that processes the SAIL data is proposed. The basic principle, design equations, and necessary analysis are presented. To verify this principle scheme, an experimental optical SAIL processor setup is constructed. The imaging results of SAIL data obtained by our SAIL demonstrator are presented. The optical processor is compact, lightweight, and consumes low power. This optical processor can also provide inherent parallel and speed-of-light computing capability, and thus has potential applications in on-board and satellite-borne SAIL systems. PMID:24663462
Sun, Zhiwei; Hou, Peipei; Zhi, Ya'nan; Sun, Jianfeng; Zhou, Yu; Xu, Qian; Lu, Zhiyong; Liu, Liren
Eyesafe ladar testbed with coaxial color imager
A new experimental full-waveform LADAR system has been developed that fuses a pixel-aligned color imager within the same optical path. The Eye-safe LADAR Test-bed (ELT) consists of a single beam energy-detection LADAR that raster scans within the same field of view as an aperture-sharing color camera. The LADAR includes a pulsed 1.54 ?m Erbium-doped fiber laser; a high-bandwidth receiver; a fine steering mirror for raster scanning; and a ball joint gimbal mirror for steering over a wide field of regard are all used. The system has a 6 inch aperture and the LADAR has pulse rate of up to 100 kHz. The color imager is folded into the optical path via a cold mirror. A novel feature of the ELT is its ability to capture LADAR and color data that are registered temporally and spatially. This allows immediate direct association of LADAR-derived 3D point coordinates with pixel coordinates of the color imagery. The mapping allows accurate pointing of the instrument at targets of interest and immediate insight into the nature and source of the LADAR phenomenology observed. The system is deployed on a custom van designed to enable experimentation with a variety of objects.
Pack, Robert T.; Swasey, Jason; Fullmer, Rees; Budge, Scott; Israelsen, Paul; Petersen, Brad; Cook, Dean
Construction of multi-functional open modulized Matlab simulation toolbox for imaging ladar system
Ladar system simulation is to simulate the ladar models using computer simulation technology in order to predict the performance of the ladar system. This paper presents the developments of laser imaging radar simulation for domestic and overseas studies and the studies of computer simulation on ladar system with different application requests. The LadarSim and FOI-LadarSIM simulation facilities of Utah State University and Swedish Defence Research Agency are introduced in details. This paper presents the low level of simulation scale, un-unified design and applications of domestic researches in imaging ladar system simulation, which are mostly to achieve simple function simulation based on ranging equations for ladar systems. Design of laser imaging radar simulation with open and modularized structure is proposed to design unified modules for ladar system, laser emitter, atmosphere models, target models, signal receiver, parameters setting and system controller. Unified Matlab toolbox and standard control modules have been built with regulated input and output of the functions, and the communication protocols between hardware modules. A simulation based on ICCD gain-modulated imaging ladar system for a space shuttle is made based on the toolbox. The simulation result shows that the models and parameter settings of the Matlab toolbox are able to simulate the actual detection process precisely. The unified control module and pre-defined parameter settings simplify the simulation of imaging ladar detection. Its open structures enable the toolbox to be modified for specialized requests. The modulization gives simulations flexibility.
Wu, Long; Zhao, Yuan; Tang, Meng; He, Jiang; Zhang, Yong
Utilization of flash ladar for cooperative and uncooperative rendezvous and capture
An ideal Rendezvous and Capture (R&C) sensor on a seeker Space Vehicle (SV) would provide accurate relative 6 degree of freedom data for the Guidance Navigation and Control System (GNCS) from far and near, operate autonomously, and provide multifunctional capability. Flash LADAR has the potential to fulfill these requirements. Sandia has developed Scannerless Range Imaging (SRI) LADAR sensors for a multitude of applications. One of the sensors, LDRI, flew onboard the STS97 mission to install the P6 truss and solar panels on the International Space Station. When compared to scanning LADAR, Scannerless LADAR is smaller, lighter, not mechanically complex, and has a much faster image acquisition time. Recently Sandia has demonstrated Flash Scannerless Range Imaging. Flash LADAR enables the capture of a full scene 3-D range image in one acquisition, thus, enabling freeze motion. The technology"s proven ability to accurately image an object as well as capture the image on the move has the potential to provide very accurate static and dynamic position data for the target vehicle relative to the seeker SV. Since no specific requirements are imposed on the target vehicle, the sensor will work equally well on cooperative and uncooperative target vehicles. This sensor technology can also provide docking feature inspection data and perform a detailed inspection of the target vehicle. This paper will describe the applicability of a Flash LADAR sensor for on-orbit cooperative and uncooperative rendezvous and capture.
Habbit, Robert D., Jr.; Nellums, Robert O.; Niese, Aaron D.; Rodriguez, Jose L.
Limitations of Geiger-mode arrays for Flash LADAR applications
It is shown through physics-based Monte Carlo simulations of avalanche photodiode (APD) LADAR receivers that under typical operating scenarios, Geiger-mode APD (GmAPD) flash LADAR receivers may often be ineffective. These results are corroborated by analysis of the signal photon detection efficiency and signal-to-noise ratio metrics. Due to their ability to detect only one pulse per laser shot, the target detection efficiency of GmAPD receivers, as measured by target signal events detected compared to those present at the receiver's optical aperture, is shown to be highly particular and respond nonlinearly to the specific LADAR conditions including range, laser power, detector efficiency, and target occlusion, which causes the GmAPD target detection capabilities to vary unpredictably over standard mission conditions. In the detection of partially occluded targets, GmAPD LADAR receivers perform optimally within only a narrow operating window of range, detector efficiency, and laser power; outside this window performance degrades sharply. Operating at both short and long standoff ranges, GmAPD receivers most often cannot detect partially occluded targets, and with an increased number of detector dark noise events, e.g. resulting from exposure to ionizing radiation, the probability that a GmAPD device is armed and able to detect target signal returns approaches zero. Even when multiple pulses are accumulated or contrived operational scenarios are employed, and even in weak-signal scenarios, GmAPDs most often perform inefficiently in their detection of target signal events at the aperture. It is concluded that the inability of the GmAPD to detect target signal present at the receiver's aperture may lead to a loss of operational capability, may have undesired implications for the equivalent optical aperture, laser power, and/or system complexity, and may incur other costs deleterious to operational efficacy.
Williams, George M., Jr.
Waveform comparison for coherent ladar imaging using a helicopter facet model target
A facet model of a helicopter containing 35,000 facets is used to compare coherent ladar waveform performance in precision and in resolution. The helicopter represents a convenient man made object for these tests. Several coherent ladar waveforms have been compared previously applying "range-resolved Doppler and intensity" (RRDI) or "inverse synthetic aperture ladar" (ISAR) algorithms in order to numerically construct an image of the target in slant-range and Doppler frequency spread. The targets are generally at large distances and are much smaller than the diffraction limited laser spot size or the diffraction limited detector's field-of-view. In this study we emphasize the "tangent-FM" waveform and review its performance relative to other waveforms. Note that thousands of facet models of interest are available on the internet and are usually low cost or even free. We also utilized a new "analytic signal" construction, recently published, for a small improvement in the final image quality.
Youmans, Douglas G.
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USU LadarSIM Release 2.0 is a ladar simulator that has the ability to feed high-level mission scripts into a processor that automatically generates scan commands during flight simulations. The scan generation depends on specified flight trajectories and scenes consisting of terrain and targets. The scenes and trajectories can either consist of simulated or actual data. The first modeling step produces an outline of scan footprints in xyz space. Once mission goals have been analyzed and it is ...
Pack, R. T.; Saunders, D.; Fullmer, R. R.; Budge, S. E.
Characterization of articulated vehicles using ladar seekers
Many vehicle targets of interest to military automatic target recognition (ATR) possess articulating components: that is, they have components that change position relative to the main body. Many vehicles also have multiple configurations wherein one or more devices or objects may be added to enhance specific military or logistical capabilities. As the expected target set for military ATR becomes more comprehensive, many additional articulations and optional components must be handled. Mobile air defense units often include moving radar antennae as well as turreted guns and missile launchers. Surface-to-surface missile launchers may be encountered with or without missiles, and with the launch rails raised or lowered. Engineers and countermine vehicles have a tremendous number of possible configurations and even conventional battle tanks may very items such as external reactive armor, long- range tanks, turret azimuth, and gun elevation. These changes pose a significant barrier to the target identification process since they greatly increase the range of possible target signatures. When combined with variations already encountered due to target aspect changes, an extremely large number of possible signatures is formed. Conventional algorithms cannot process so many possibilities effectively, so in response, the matching process is often made less selective. This degrades identification performance, increase false alarm rates, and increases data requirements for algorithm testing and training. By explicitly involving articulation in the detection and identification stages of an ATR algorithm, more precise matching constraints can be applied, and better selectivity can be achieve. Additional benefits include the measurement of the position and orientation of articulated components, which often has tactical significance. In this paper, the result of a study investigating the impact of target articulation in ATR for military vehicles are presented. 3D ladar signature data is used. An algorithmic solution is proposed and directions for further research are noted.
Wellfare, Michael R.; Norris-Zachery, Karen
Demonstration of advanced solid state ladar (DASSL)
The Armament Directorate of Wright Laboratory is tasked with pursuing technologies that lead towards autonomous guidance for conventional munitions. Seeker technologies pursued include SAR, imaging infrared, millimeter wave, and laser radar seekers. Laser Radar, or LADAR, systems using uncooled diode pumped solid state lasers operating around 1 micrometers are active sensors providing high resolution range and intensity imagery. LADAR is not susceptible to variations common to thermal IR systems, allowing greater simplicity of autonomous target acquisition algorithms. Therefore, LADAR sensors combined with advanced algorithms provide robust seeker technology capable of autonomous precision guidance. The small smart bomb (SSB) is a next generation weapon concept requiring this precision guidance. The 250 pound SSB penetrator provides the lethality of 2000 pound penetrators by delivering 50 pounds of high explosive with surgical precision. Space limitations, tightly controlled impact conditions, and high weapon velocities suggest laser radar as a candidate seeker. This paper discusses phase I of the DASSL program in which SSB weapon requirements are flowed down to seeker requirements through a structured system requirement analysis, and discusses how these seeker requirements affect seeker design.
Broome, Kent W.; Carstens, Anne M.; Hudson, J. Roger; Yates, Kenneth L.
Fusion of ladar and SAR for terminal guidance
A concept for integrating the airborne sensing capabilities of synthetic aperture radar (SAR) with laser detection and ranging (LADAR) for terminal guidance is presented. The advantages of each technology in the reconnaissance and terminal guidance roles for target acquisition are exploited. The concept is directed at terminal guidance against fixed and quasi-fixed targets (i.e., targets expected to be in approximately the same location and orientation from the time of reconnaissance to the time of targeting). The advantages of airborne SAR are high resolution, all-weather, standoff reconnaissance capabilities. The advantages of LADAR are high resolution in the real aperture mode using moderately sized and priced optics, and good performance over modest ranges (on the order of a kilometer or less). Within the concept, LADAR would provide terminal guidance using two SAR provided data sets: (1) target estimated coordinates, and (2) the SAR imagery of target/surround. Technical risks are: lack of a demonstrated capability for SAR-to-LADAR image correlation; lack of analysis of low- cost, light weight LADAR and real-time correlators; and lack of analysis of adequate signal- to-noise in a range of atmospheric environments. This paper is directed at the use of SAR image by the LADAR for aim point refinement. It addresses geometric differences in the SAR and LADAR images, the effect of different reflectances on scene segmentation, and the basis for an approach for developing common geometric projections for the SAR and LADAR image correlations.
Cress, Daniel H.; Mastin, Gary A.
Real-time scene and signature generation for ladar and imaging sensors
This paper describes development of two key functionalities within the VIRSuite scene simulation program, broadening its scene generation capabilities and increasing accuracy of thermal signatures. Firstly, a new LADAR scene generation module has been designed. It is capable of simulating range imagery for Geiger mode LADAR, in addition to the already existing functionality for linear mode systems. Furthermore, a new 3D heat diffusion solver has been developed within the VIRSuite signature prediction module. It is capable of calculating the temperature distribution in complex three-dimensional objects for enhanced dynamic prediction of thermal signatures. With these enhancements, VIRSuite is now a robust tool for conducting dynamic simulation for missiles with multi-mode seekers.
Swierkowski, Leszek; Christie, Chad L.; Antanovskii, Leonid; Gouthas, Efthimios
Segmentation, classification, and pose estimation of maritime targets in flash-ladar imagery
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The paper presents new techniques for automatic segmentation, classification, and generic pose estimation of ships and boats in laser radar imagery. Segmentation, which primarily involves elimination of water reflections, is based on modeling surface waves and comparing the expected water reflection signature to the ladar intensity image. Shape classification matches a parametric shape representation of a generic ship hull with parameters extracted from the range image. The extracted paramete...
Armbruster, Walter; Hammer, Marcus
Real-time 3D flash ladar imaging through GPU data processing
We present real-time 3D image processing of flash ladar data using our recently developed GPU parallel processing kernels. Our laboratory and airborne experiences with flash ladar focal planes have shown that per laser flash, typically only a small fraction of the pixels on the focal plane array actually produce a meaningful range signal. Therefore, to optimize overall data processing speed, the large quantity of uninformative data are filtered out and removed from the data stream prior to the mathematically intensive point cloud transformation processing. This front-end pre-processing, which largely consists of control flow instructions, is specific to the particular type of flash ladar focal plane array being used and is performed by the computer's CPU. The valid signals along with their corresponding inertial and navigation metadata are then transferred to a GPU device to perform range-correction, geo-location, and ortho-rectification on each 3D data point so that data from multiple frames can be properly tiled together either to create a wide-area map or to reconstruct an object from multiple look angles. GPU parallel processing kernels were developed using OpenCL. Postprocessing to perform fine registration between data frames via complex iterative steps also benefits greatly from this type of high-performance computing. The performance improvements obtained using GPU processing to create corrected 3D images and for frame-to-frame fine-registration are presented.
Wong, Chung M.; Bracikowski, Christopher; Baldauf, Brian K.; Havstad, Steven A.
The single photon sensitivity of Geiger-mode avalanche photo diodes (GmAPDs) has facilitated the development of LADAR systems that operate at longer stand-off distances, require lower laser pulse powers and are capable of imaging through a partial obscuration. In this paper, we describe a GmAPD LADAR system which operates at the eye-safe wavelength of 1541 nm. The longer wavelength should enhance system covertness and improve haze penetration compared to systems using 1064 nm lasers. The system is comprised of a COTS 1541 nm erbium fiber laser producing 4 ns pulses at 80 kHz to 450 kHz and a COTS camera with a focal plane of 32x32 InGaAs GmAPDs band-gap optimized for 1550 nm. Laboratory characterization methodology and results are discussed. We show that accurate modeling of the system response, allows us to achieve a depth resolution which is limited by the width of the camera's time bin (.25 ns or 1.5 inches) rather than by the duration of the laser pulse (4 ns or 2 ft.). In the presence of obscuration, the depth discrimination is degraded to 6 inches but is still significantly better than that dictated by the laser pulse duration. We conclude with a discussion of future work.
Kutteruf, Mary R.; Lebow, Paul
A low-power CMOS trans-impedance amplifier for FM/cw ladar imaging system
A scannerless ladar imaging system based on a unique frequency modulation/continuous wave (FM/cw) technique is able to entirely capture the target environment, using a focal plane array to construct a 3D picture of the target. This paper presents a low power trans-impedance amplifier (TIA) designed and implemented by 0.18 ?m CMOS technology, which is used in the FM/cw imaging ladar with a 64×64 metal-semiconductor-metal(MSM) self-mixing detector array. The input stage of the operational amplifier (op amp) in TIA is realized with folded cascade structure to achieve large open loop gain and low offset. The simulation and test results of TIA with MSM detectors indicate that the single-end trans-impedance gain is beyond 100 k?, and the -3 dB bandwidth of Op Amp is beyond 60 MHz. The input common mode voltage ranges from 0.2 V to 1.5 V, and the power dissipation is reduced to 1.8 mW with a supply voltage of 3.3 V. The performance test results show that the TIA is a candidate for preamplifier of the read-out integrated circuit (ROIC) in the FM/cw scannerless ladar imaging system.
Hu, Kai; Zhao, Yi-qiang; Sheng, Yun; Zhao, Hong-liang; Yu, Hai-xia
Characterization of a scannerless LADAR system. [Laser Radar System (LADAR)
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Performance projections based on the analytical model of a scannerless laser radar system are compared to laboratory simulations and to field data measurements. Data and characteristics of the system, including camera response, image spatial resolution, and contributions to the signal-to-noise ratio are presented. A discussion of range resolution for this system will also be presented, and finally, the performance characteristics of the prototype benchtop system will be summarized.
Garcia, P.; Anthes, J.; Pierce, J.T.; Dressendorfer, P.; Evans, I.K.; Bradley, B.D.; Sackos, J.T.; LeCavalier, M.M.
Ladar ATR via probabilistic open set techniques
Target recognition algorithms trained using finite sets of target and confuser data result in classifiers limited by the training set. Algorithms trained under closed set assumptions do not account for the infinite universe of confusers found in practice. In contrast, classification algorithms developed under open set assumptions label inputs not present in the training data as unknown instead of assigning the most likely class. We present an approach to open set recognition that utilizes class posterior estimates to determine probability thresholds for classification. This is accomplished by first training a support vector machine (SVM) in a 1-vs-all configuration on a training dataset containing only target classes. A validation set containing only class data belonging to the training set is used to iteratively determine appropriate posterior probability thresholds for each target class. The testing dataset, which contains targets present in the training data as well as several confuser classes, is first classified by the 1-vs-all SVM. If the estimated posterior for an input falls below the threshold, the target is labeled as unknown. Otherwise, it is labeled with the class resulting from the SVM decision. We apply our method to automatic target recognition (ATR) of ladar range images and compare its performance to current open set and closed set recognition techniques.
Scherreik, Matthew; Rigling, Brian
In this study of atmospheric effects on Geiger Mode laser ranging and detection (LADAR), the parameter space is explored primarily using the Air Force Institute of Technology Center for Directed Energy's (AFIT/CDE) Laser Environmental Effects Definition and Reference (LEEDR) code. The expected performance of LADAR systems is assessed at operationally representative wavelengths of 1.064, 1.56 and 2.039 ?m at a number of locations worldwide. Signal attenuation and background noise are characterized using LEEDR. These results are compared to standard atmosphere and Fast Atmospheric Signature Code (FASCODE) assessments. Scenarios evaluated are based on air-toground engagements including both down looking oblique and vertical geometries in which anticipated clear air aerosols are expected to occur. Engagement geometry variations are considered to determine optimum employment techniques to exploit or defeat the environmental conditions. Results, presented primarily in the form of worldwide plots of notional signal to noise ratios, show a significant climate dependence, but large variances between climatological and standard atmosphere assessments. An overall average absolute mean difference ratio of 1.03 is found when climatological signal-to-noise ratios at 40 locations are compared to their equivalent standard atmosphere assessment. Atmospheric transmission is shown to not always correlate with signal-to-noise ratios between different atmosphere profiles. Allowing aerosols to swell with relative humidity proves to be significant especially for up looking geometries reducing the signal-to-noise ratio several orders of magnitude. Turbulence blurring effects that impact tracking and imaging show that the LADAR system has little capability at a 50km range yet the turbulence has little impact at a 3km range.
Roth, Benjamin D.; Fiorino, Steven T.
Object-based change detection and classification
The paper presents some recent developments on object-based change detection and classification. In detail, the following algorithms were implemented either as Matlab or IDL programmes or as plug-ins for Definiens Developer: i) object-based change detection: segmentation of bitemporal datasets, change detection using the Multivariate Alteration Detection1 based on object features; ii) object features and object feature extraction: moment invariants, automated extraction of object features using Bayesian statistics; iii) object-based classification by neural networks: FFN and Class- dependent FFN using five different learning algorithms. The paper introduces the methodologies, describes the implementation and gives some examples results on the application.
Niemeyer, Irmgard; Bachmann, Florian; John, André; Listner, Clemens; Marpu, Prashanth Reddy
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Full Text Available OBJETIVO:Avaliar os resultados do retratamento convencional (LADAR, Alcon e do retratamento personalizado(LADARWave, Alcon em olhos submetidos a LASIK primário convencional. MÉTODOS: Estudo retrospectivo de revisăo de prontuários consecutivos, de 38 olhos em 38 pacientes, submetidos a retratamento de LASIK para correçăo de miopia e astigmatismo. Os olhos operados foram divididos em dois grupos iguais. No primeiro grupo foi realizado o retratamento personalizado e, no outro, o retratamento convencional. As seguintes variáveis foram comparadas: acuidade visual de alto contraste e refraçăo manifesta. A qualidade visual foi estimada e comparada através de inquérito subjetivo proposto aos pacientes. RESULTADOS: Năo houve diferença estatística entre os grupos comparando-se as variáveis estudadas. O equivalente esférico pós-retratamento foi de 0,36 no grupo convencional e de 0,47 no personalizado (p=0,079. A acuidade visual de Snellen foi de 0,91 e 0,87, respectivamente, com p=0,07. O total de aberraçőes pré-operatório foi maior do que o pós-operatório no grupo personalizado (pOBJECTIVE: To evaluate the results of conventional (Ladar, Alcon and customized (LADARWave, Alcon retreatment ineyes undergoing conventional primary LASIK. METHODS: Retrospective revision of consecutive clinical report forms of 38 eyes of 38 patients who underwent LASIK retreatment for myopia and astigmatism. The operated eyes were divided into two equal groups. In the first was performed customized retreatment and, in the other, conventional retreatment. The following variables were compared: high contrast visual acuity and manifest refraction. The visual quality was estimated and compared using subjective survey offered to patients. RESULTS: There was no statistical difference between the groups when comparing the variables studied. The spherical equivalent after retreatment was 0.36 in the conventional group and 0.47 in the custom (p = 0.079. Snelen visual acuity was 0.91 and 0.87, respectively (p = 0.07. The preoperative total aberrations was higher than the postoperative period in custom group (p <0.001. In the conventional group there was no difference for any aberration evaluated. Complaints of glare (p = 0.117, photophobia (p = 0.987 and vision fluctuation (p = 0.545 were statistically similar between the two groups. CONCLUSION: Comparing the custom and conventional surgery for primary LASIK retreatment with LADAR, Alcon, there wasno statistical difference in the quantity and quality of vision. Nevertheless, there was a higher percentage of patients with complaints in relation to the visual quality in the group undergoing conventional surgery. Custom surgery seems to have greater capacity to reduce the total aberrations than conventional.
Lucas Monferrari Monteiro Vianna
While a Gated-Viewing system primarily provides the intensity values of the captured laser radiation, it is also possible to determine range information in a static scenario by the sliding gates method. In this paper, we compare this method to a time-of-flight based 3-D Flash LADAR technique in terms of range accuracy under moderate and strong turbulence conditions. The first method requires several Gated-Viewing images (several laser pulses) with stepwise increased gate delay times. For a 3-D Flash LADAR system, one laser pulse is sufficient because for each pixel the range is determined by the time-of-flight method. We have combined a Gated-Viewing camera (640 × 480 pixels) as well as a 3-D Flash LADAR camera (128 × 128 pixels) with a pulsed 1.57 ?m laser source. The maximal laser pulse energy was 67 mJ. We have conducted field measurements at different times of day. Two reflectance panels and a vehicle at a distance of 2 km were recorded. The plates were positioned diagonal to the line of sight with an angle of about 45 degrees in order to determine range accuracies. In addition, a laser scintillometer provided atmospheric turbulence strength along the propagation path.
Göhler, Benjamin; Lutzmann, Peter
Simulation of a Geiger-Mode Imaging LADAR System for Performance Assessment
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Full Text Available As LADAR systems applications gradually become more diverse, new types of systems are being developed. When developing new systems, simulation studies are an essential prerequisite. A simulator enables performance predictions and optimal system parameters at the design level, as well as providing sample data for developing and validating application algorithms. The purpose of the study is to propose a method for simulating a Geiger-mode imaging LADAR system. We develop simulation software to assess system performance and generate sample data for the applications. The simulation is based on three aspects of modeling—the geometry, radiometry and detection. The geometric model computes the ranges to the reflection points of the laser pulses. The radiometric model generates the return signals, including the noises. The detection model determines the flight times of the laser pulses based on the nature of the Geiger-mode detector. We generated sample data using the simulator with the system parameters and analyzed the detection performance by comparing the simulated points to the reference points. The proportion of the outliers in the simulated points reached 25.53%, indicating the need for efficient outlier elimination algorithms. In addition, the false alarm rate and dropout rate of the designed system were computed as 1.76% and 1.06%, respectively.
Yong Joon Kwon
Perceptual Load Modulates Object-Based Attention
Two experimental series are reported using both reaction time (RT) and a data-limited perceptual report to examine the effects of perceptual load on object-based attention. Perceptual load was manipulated across 3 levels by increasing the complexity of perceptual judgments. Data from the RT-based experiments showed object-based effects when the…
Ho, Ming-Chou; Atchley, Paul
Object-Based Attention and Cognitive Tunneling
Simulator-based research has shown that pilots cognitively tunnel their attention on head-up displays (HUDs). Cognitive tunneling has been linked to object-based visual attention on the assumption that HUD symbology is perceptually grouped into an object that is perceived and attended separately from the external scene. The present research…
Jarmasz, Jerzy; Herdman, Chris M.; Johannsdottir, Kamilla Run
Object-based neglect in number processing
Digital Repository Infrastructure Vision for European Research (DRIVER)
Abstract Recent evidence suggests that neglect patients seem to have particular problems representing relatively smaller numbers corresponding to the left part of the mental number line. However, while this indicates space-based neglect for representational number space little is known about whether and - if so - how object-based neglect influences number processing. To evaluate influences of object-based neglect in numerical cognition, a group of neglect patients and two con...
Klein Elise; Moeller Korbinian; Haider Christine; Gassner Alfred; Nuerk Hans-Christoph
Geiger-mode APD camera system for single-photon 3D LADAR imaging
The unparalleled sensitivity of 3D LADAR imaging sensors based on single photon detection provides substantial benefits for imaging at long stand-off distances and minimizing laser pulse energy requirements. To obtain 3D LADAR images with single photon sensitivity, we have demonstrated focal plane arrays (FPAs) based on InGaAsP Geiger-mode avalanche photodiodes (GmAPDs) optimized for use at either 1.06 ?m or 1.55 ?m. These state-of-the-art FPAs exhibit excellent pixel-level performance and the capability for 100% pixel yield on a 32 x 32 format. To realize the full potential of these FPAs, we have recently developed an integrated camera system providing turnkey operation based on FPGA control. This system implementation enables the extremely high frame-rate capability of the GmAPD FPA, and frame rates in excess of 250 kHz (for 0.4 ?s range gates) can be accommodated using an industry-standard CameraLink interface in full configuration. Real-time data streaming for continuous acquisition of 2 ?s range gate point cloud data with 13-bit time-stamp resolution at 186 kHz frame rates has been established using multiple solid-state storage drives. Range gate durations spanning 4 ns to 10 ?s provide broad operational flexibility. The camera also provides real-time signal processing in the form of multi-frame gray-scale contrast images and single-frame time-stamp histograms, and automated bias control has been implemented to maintain a constant photon detection efficiency in the presence of ambient temperature changes. A comprehensive graphical user interface has been developed to provide complete camera control using a simple serial command set, and this command set supports highly flexible end-user customization.
Entwistle, Mark; Itzler, Mark A.; Chen, Jim; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir
The Eyesafe Ladar Test-bed (ELT) is an experimental ladar system with the capability of digitizing return laser pulse waveforms at 2 GHz. These waveforms can then be exploited off-line in the laboratory to develop signal processing techniques for noise reduction, range resolution improvement, and range discrimination between two surfaces of similar range interrogated by a single laser pulse. This paper presents the results of experiments with new deconvolution algorithms with the hoped-for gains of improving the range discrimination of the ladar system. The sparsity of ladar returns is exploited to solve the deconvolution problem in two steps. The first step is to estimate a point target response using a database of measured calibration data. This basic target response is used to construct a dictionary of target responses with different delays/ranges. Using this dictionary ladar returns from a wide variety of surface configurations can be synthesized by taking linear combinations. A sparse linear combination matches the physical reality that ladar returns consist of the overlapping of only a few pulses. The dictionary construction process is a pre-processing step that is performed only once. The deconvolution step is performed by minimizing the error between the measured ladar return and the dictionary model while constraining the coefficient vector to be sparse. Other constraints such as the non-negativity of the coefficients are also applied. The results of the proposed technique are presented in the paper and are shown to compare favorably with previously investigated deconvolution techniques.
Budge, Scott E.; Gunther, Jacob H.
Digital Repository Infrastructure Vision for European Research (DRIVER)
While a Gated-Viewing system primarily provides the intensity values of the captured laser radiation, it is also possible to determine range information in a static scenario by the sliding gates method. In this paper, we compare this method to a time-of-flight based 3-D Flash LADAR technique in terms of range accuracy under moderate and strong turbulence conditions. The first method requires several Gated-Viewing images (several laser pulses) with stepwise increased gate delay times. For a 3-...
Go?hler, B.; Lutzmann, P.
Scientific Electronic Library Online (English)
Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese OBJETIVO:Avaliar os resultados do retratamento convencional (LADAR, Alcon) e do retratamento personalizado(LADARWave, Alcon) em olhos submetidos a LASIK primário convencional. MÉTODOS: Estudo retrospectivo de revisăo de prontuários consecutivos, de 38 olhos em 38 pacientes, submetidos a retratamento [...] de LASIK para correçăo de miopia e astigmatismo. Os olhos operados foram divididos em dois grupos iguais. No primeiro grupo foi realizado o retratamento personalizado e, no outro, o retratamento convencional. As seguintes variáveis foram comparadas: acuidade visual de alto contraste e refraçăo manifesta. A qualidade visual foi estimada e comparada através de inquérito subjetivo proposto aos pacientes. RESULTADOS: Năo houve diferença estatística entre os grupos comparando-se as variáveis estudadas. O equivalente esférico pós-retratamento foi de 0,36 no grupo convencional e de 0,47 no personalizado (p=0,079). A acuidade visual de Snellen foi de 0,91 e 0,87, respectivamente, com p=0,07. O total de aberraçőes pré-operatório foi maior do que o pós-operatório no grupo personalizado (p Abstract in english OBJECTIVE: To evaluate the results of conventional (Ladar, Alcon) and customized (LADARWave, Alcon) retreatment ineyes undergoing conventional primary LASIK. METHODS: Retrospective revision of consecutive clinical report forms of 38 eyes of 38 patients who underwent LASIK retreatment for myopia and [...] astigmatism. The operated eyes were divided into two equal groups. In the first was performed customized retreatment and, in the other, conventional retreatment. The following variables were compared: high contrast visual acuity and manifest refraction. The visual quality was estimated and compared using subjective survey offered to patients. RESULTS: There was no statistical difference between the groups when comparing the variables studied. The spherical equivalent after retreatment was 0.36 in the conventional group and 0.47 in the custom (p = 0.079). Snelen visual acuity was 0.91 and 0.87, respectively (p = 0.07). The preoperative total aberrations was higher than the postoperative period in custom group (p
Lucas Monferrari Monteiro, Vianna; Heloisa Moraes do, Nascimento; Mauro, Campos.
Scientific Electronic Library Online (English)
Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese OBJETIVO:Avaliar os resultados do retratamento convencional (LADAR, Alcon) e do retratamento personalizado(LADARWave, Alcon) em olhos submetidos a LASIK primário convencional. MÉTODOS: Estudo retrospectivo de revisăo de prontuários consecutivos, de 38 olhos em 38 pacientes, submetidos a retratamento [...] de LASIK para correçăo de miopia e astigmatismo. Os olhos operados foram divididos em dois grupos iguais. No primeiro grupo foi realizado o retratamento personalizado e, no outro, o retratamento convencional. As seguintes variáveis foram comparadas: acuidade visual de alto contraste e refraçăo manifesta. A qualidade visual foi estimada e comparada através de inquérito subjetivo proposto aos pacientes. RESULTADOS: Năo houve diferença estatística entre os grupos comparando-se as variáveis estudadas. O equivalente esférico pós-retratamento foi de 0,36 no grupo convencional e de 0,47 no personalizado (p=0,079). A acuidade visual de Snellen foi de 0,91 e 0,87, respectivamente, com p=0,07. O total de aberraçőes pré-operatório foi maior do que o pós-operatório no grupo personalizado (p Abstract in english OBJECTIVE: To evaluate the results of conventional (Ladar, Alcon) and customized (LADARWave, Alcon) retreatment ineyes undergoing conventional primary LASIK. METHODS: Retrospective revision of consecutive clinical report forms of 38 eyes of 38 patients who underwent LASIK retreatment for myopia and [...] astigmatism. The operated eyes were divided into two equal groups. In the first was performed customized retreatment and, in the other, conventional retreatment. The following variables were compared: high contrast visual acuity and manifest refraction. The visual quality was estimated and compared using subjective survey offered to patients. RESULTS: There was no statistical difference between the groups when comparing the variables studied. The spherical equivalent after retreatment was 0.36 in the conventional group and 0.47 in the custom (p = 0.079). Snelen visual acuity was 0.91 and 0.87, respectively (p = 0.07). The preoperative total aberrations was higher than the postoperative period in custom group (p
Lucas Monferrari Monteiro, Vianna; Heloisa Moraes do, Nascimento; Mauro, Campos.
Advances in ladar components and subsystems at Raytheon
Raytheon is developing NIR sensor chip assemblies (SCAs) for scanning and staring 3D LADAR systems. High sensitivity is obtained by integrating high performance detectors with gain, i.e., APDs with very low noise Readout Integrated Circuits (ROICs). Unique aspects of these designs include: independent acquisition (non-gated) of pulse returns, multiple pulse returns with both time and intensity reported to enable full 3D reconstruction of the image. Recent breakthrough in device design has resulted in HgCdTe APDs operating at 300K with essentially no excess noise to gains in excess of 100, low NEP linear mode photon counting. SCAs utilizing these high performance APDs have been integrated and demonstrated excellent spatial and range resolution enabling detailed 3D imagery both at short range and long ranges. In the following we will review progress in real-time 3D LADAR imaging receiver products in three areas: (1) scanning 256 × 4 configuration for the Multi-Mode Sensor Seeker (MMSS) program and (2) staring 256 × 256 configuration for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) lunar landing mission and (3) Photon-Counting SCAs which have demonstrated a dramatic reduction in dark count rate due to improved design, operation and processing.
Jack, Michael; Chapman, George; Edwards, John; Mc Keag, William; Veeder, Tricia; Wehner, Justin; Roberts, Tom; Robinson, Tom; Neisz, James; Andressen, Cliff; Rinker, Robert; Hall, Donald N. B.; Jacobson, Shane M.; Amzajerdian, Farzin; Cook, T. Dean
Object-based neglect in number processing
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Full Text Available Abstract Recent evidence suggests that neglect patients seem to have particular problems representing relatively smaller numbers corresponding to the left part of the mental number line. However, while this indicates space-based neglect for representational number space little is known about whether and - if so - how object-based neglect influences number processing. To evaluate influences of object-based neglect in numerical cognition, a group of neglect patients and two control groups had to compare two-digit numbers to an internally represented standard. Conceptualizing two-digit numbers as objects of which the left part (i.e., the tens digit should be specifically neglected we were able to evaluate object-based neglect for number magnitude processing. Object-based neglect was indicated by a larger unit-decade compatibility effect actually reflecting impaired processing of the leftward tens digits. Additionally, faster processing of within- as compared to between-decade items provided further evidence suggesting particular difficulties in integrating tens and units into the place-value structure of the Arabic number system. In summary, the present study indicates that, in addition to the spatial representation of number magnitude, also the processing of place-value information of multi-digit numbers seems specifically impaired in neglect patients.
Klein Elise
Comb-calibrated frequency-modulated continuous-wave ladar for absolute distance measurements.
We demonstrate a comb-calibrated frequency-modulated continuous-wave laser detection and ranging (FMCW ladar) system for absolute distance measurements. The FMCW ladar uses a compact external cavity laser that is swept quasi-sinusoidally over 1 THz at a 1 kHz rate. The system simultaneously records the heterodyne FMCW ladar signal and the instantaneous laser frequency at sweep rates up to 3400 THz/s, as measured against a free-running frequency comb (femtosecond fiber laser). Demodulation of the ladar signal against the instantaneous laser frequency yields the range to the target with 1 ms update rates, bandwidth-limited 130 ?m resolution and a ~100 nm accuracy that is directly linked to the counted repetition rate of the comb. The precision is <100 nm at the 1 ms update rate and reaches ~6 nm for a 100 ms average. PMID:23938965
Baumann, Esther; Giorgetta, Fabrizio R; Coddington, Ian; Sinclair, Laura C; Knabe, Kevin; Swann, William C; Newbury, Nathan R
Simulation of a Geiger-Mode Imaging LADAR System for Performance Assessment
Digital Repository Infrastructure Vision for European Research (DRIVER)
As LADAR systems applications gradually become more diverse, new types of systems are being developed. When developing new systems, simulation studies are an essential prerequisite. A simulator enables performance predictions and optimal system parameters at the design level, as well as providing sample data for developing and validating application algorithms. The purpose of the study is to propose a method for simulating a Geiger-mode imaging LADAR system. We develop simulation software to ...
Yong Joon Kwon; Impyeong Lee; Seongjoon Kim
Quantum enhancement of a coherent ladar receiver using phase-sensitive amplification
Digital Repository Infrastructure Vision for European Research (DRIVER)
We demonstrate a balanced-homodyne LADAR receiver employing a phase-sensitive amplifier (PSA) to raise the effective photon detection efficiency (PDE) to nearly 100%. Since typical LADAR receivers suffer from losses in the receive optical train that routinely limit overall PDE to less than 50% thus degrading SNR, PSA can provide significant improvement through amplification with noise figure near 0 dB. Receiver inefficiencies arise from sub-unity quantum efficiency, array fill factors, signal...
Shapiro, Jeffrey H.; Nair, Ranjith; Yen, Brent J.; Wong, Franco N. C.; Wasilousky, Peter A.; Smith, Kevin H.; Glasser, Ryan; Burdge, Geoffrey L.; Burberry, Lee; Deibner, Bill; Silver, Michael; Peach, Robert C.; Visone, Christopher; Kumar, Prem; Lim, Oo-kaw
Object-based representations of spatial images
Object based representations of image data enable new content-related functionalities while facilitating management of large image databases. Developing such representations for multi-date and multi-spectral images is one of the objectives of the second phase of the Alexandria Digital Library (ADL) project at UCSB. Image segmentation and image registration are two of the main issues that are to be addressed in creating localized image representations. We present in this paper some of the recent and current work by the ADL's image processing group on robust image segmentation, registration, and the use of image texture for content representation. Built upon these technologies are techniques for managing large repositories of data. A texture thesaurus assists in creating a semantic classification of image regions. An object-based representation is proposed to facilitate data storage, retrieval, analysis, and navigation.
Newsam, Shawn; Bhagavathy, Sitaram; Kenney, Charles; Manjunath, B. S.; Fonseca, Leila
Coding Transparency in Object-Based Video
DEFF Research Database (Denmark)
A novel algorithm for coding gray level alpha planes in object-based video is presented. The scheme is based on segmentation in multiple layers. Different coders are specifically designed for each layer. In order to reduce the bit rate, cross-layer redundancies as well as temporal correlation are exploited. Coding results show the superior efficiency of the proposed scheme compared with MPEG-4
Aghito, Shankar Manuel; Forchhammer, SŘren
Invariant Object Recognition Based on Extended Fragments
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Full Text Available Visual appearance of natural objects is profoundly affected by viewing conditions such as viewpoint and illumination. Human subjects can nevertheless compensate well for variations in these viewing conditions. The strategies that the visual system uses to accomplish this are largely unclear. Previous computational studies have suggested that in principle, certain types of object fragments (rather than whole objects can be used for invariant recognition. However, whether the human visual system is actually capable of using this strategy remains unknown. Here, we show that human observers can achieve illumination invariance by using object fragments that carry the relevant information. To determine this, we have used novel, but naturalistic, 3-D visual objects called 'digital embryos'. Using novel instances of whole embryos, not fragments, we trained subjects to recognize individual embryos across illuminations. We then tested the illumination-invariant object recognition performance of subjects using fragments. We found that the performance was strongly correlated with the mutual information (MI of the fragments, provided that MI value took variations in illumination into consideration. This correlation was not attributable to any systematic differences in task difficulty between different fragments. These results reveal two important principles of invariant object recognition. First, the subjects can achieve invariance at least in part by compensating for the changes in the appearance of small local features, rather than of whole objects. Second, the subjects do not always rely on generic or pre-existing invariance of features (i.e., features whose appearance remains largely unchanged by variations in illumination, and are capable of using learning to compensate for appearance changes when necessary. These psychophysical results closely fit the predictions of earlier computational studies of fragment-based invariant object recognition.
Small Objects in Low-Earth Intersecting Ground-Based Laser Radar Operational Envelopes
NASA/Marshall Space Flight Center, in collaboration with the Air Force Research Laboratory/Directed Energy Directorate, is considering a series of experiments to demonstrate small object tracking capability. One such experiment involves a microsatellite, about 25 cm in diameter, which will be deployed from a Space Shuttle Hitchhiker canister or from an Air Force vehicle. The High Performance CO2 Ladar Surveillance Sensor (HI-CLASS) and the Advanced Electro-Optics System (AEOS) will be used to detect the micro-satellite. The goal of this paper is to determine the number of times per day that a micro-satellite orbiting at a known altitude and inclination will be visible to the laser radar, and the length of time that the micro-satellite will be visible on each pass.
Boccio, Dona V.
Object Based Middleware for Grid Computing
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Full Text Available Problem statement: ?Grid? computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications and, in some cases, high-performance orientation. The role of middleware is to ease the task of designing, programming and managing distributed applications by providing a simple, consistent and integrated distributed programming environment. Essentially, middleware is a distributed software layer, which abstracts over the complexity and heterogeneity of the underlying distributed environment with its multitude of network technologies, machine architectures, operating systems and programming languages. Approach: This study brought out the development of supportive middleware to manage resources and distributed workload across multiple administrative boundaries is of central importance to Grid computing. Active middleware services that perform look-up, scheduling and staging are being developed that allow users to identify and utilize appropriate resources that provide sustainable system and user-level qualities of service. Results: Different middleware platforms support different programming models. Perhaps the most popular model is object-based middleware in which applications are structured into objects that interact via location transparent method invocation. Conclusion: The Object Management Group?s CORBA platform offer an Interface Definition Language (IDL which is used to abstract over the fact that objects can be implemented in any suitable programming language, an object request broker which is responsible for transparently directing method invocations to the appropriate target object and a set of services such as naming, time, transactions, replication which further enhance the programming environment.
S. Muruganantham
REBOL: Relative Expression-Based Object Language
REBOL, the Relative Expression-Based Object Language, is a fascinating new scripting language developed at REBOL Technologies by Carl Sassenrath, the architect of the Amiga operating system. REBOL is intended to be used for Internet programming and, among its many features, it contains very easy-to-use networking capabilities. An example is this tiny line of REBOL code which retrieves a web page and emails it to a (fictitious) email address: "send firstname.lastname@example.com read
Sassenrath, Carl.
Atmospheric turbulence produces intensity modulation or "scintillation" effects on both on the outward laser-mode path and on the return backscattered radiation path. These both degrade laser radar (ladar) target acquisition, ranging, imaging, and feature estimation. However, the finite sized objects create scintillation averaging on the outgoing path and the finite sized telescope apertures produce scintillation averaging on the return path. We expand on previous papers going to moderate to strong turbulence cases by starting from a 20kft altitude platform and propagating at 0° elevation (with respect to the local vertical) for 100km range to a 1 m diameter diffuse sphere. The outward scintillation and inward scintillation effects, as measured at the focal plane detector array of the receiving aperture, will be compared. To eliminate hard-body surface speckle effects in order to study scintillation, Goodman's M-parameter is set to 106 in the analytical equations and the non-coherent imaging algorithm is employed in Monte Carlo realizations. The analytical equations of the signal-to-noise ratio (SNRp), or mean squared signal over a variance, for a given focal plane array pixel window of interest will be summarized and compared to Monte Carlo realizations of a 1m diffuse sphere.
Youmans, Douglas G.
Low-cost compact MEMS scanning ladar system for robotic applications
Future robots and autonomous vehicles require compact low-cost Laser Detection and Ranging (LADAR) systems for autonomous navigation. Army Research Laboratory (ARL) had recently demonstrated a brass-board short-range eye-safe MEMS scanning LADAR system for robotic applications. Boeing Spectrolab is doing a tech-transfer (CRADA) of this system and has built a compact MEMS scanning LADAR system with additional improvements in receiver sensitivity, laser system, and data processing system. Improved system sensitivity, low-cost, miniaturization, and low power consumption are the main goals for the commercialization of this LADAR system. The receiver sensitivity has been improved by 2x using large-area InGaAs PIN detectors with low-noise amplifiers. The FPGA code has been updated to extend the range to 50 meters and detect up to 3 targets per pixel. Range accuracy has been improved through the implementation of an optical T-Zero input line. A compact commercially available erbium fiber laser operating at 1550 nm wavelength is used as a transmitter, thus reducing the size of the LADAR system considerably from the ARL brassboard system. The computer interface has been consolidated to allow image data and configuration data (configuration settings and system status) to pass through a single Ethernet port. In this presentation we will discuss the system architecture and future improvements to receiver sensitivity using avalanche photodiodes.
Moss, Robert; Yuan, Ping; Bai, Xiaogang; Quesada, Emilio; Sudharsanan, Rengarajan; Stann, Barry L.; Dammann, John F.; Giza, Mark M.; Lawler, William B.
The Optical Processor Enhanced Ladar (OPEL) program is designed to evaluate the capabilities of a seeker obtained by integrating two state-of-the-art technologies, laser radar, or ladar, and optical correlation. The program is a thirty-two month effort to build, optimize, and test a breadboard seeker system (the OPEL System) that incorporates these two promising technologies. Laser radars produce both range and intensity image information. Use of this information in an optical correlator is described. A correlator with binary phase input and ternary amplitude and phase filter capability is assumed. Laser radar imagery was collected on five targets over 360 degrees of azimuth from 3 elevation angles. This imagery was then processed to provide training sets in preparation for filter construction. This paper reviews the ladar and optical correlator technologies used, outlines the OPEL program, and describes the OPEL system.
Goldstein, Dennis H.; Mills, Stuart A.; Dydyk, Robert B.
Ultra-Compact, High-Resolution LADAR System for 3D Imaging
An eye-safe LADAR system weighs under 500 grams and has range resolution of 1 mm at 10 m. This laser uses an adjustable, tiny microelectromechanical system (MEMS) mirror that was made in SiWave to sweep laser frequency. The size of the laser device is small (70x50x13 mm). The LADAR uses all the mature fiber-optic telecommunication technologies in the system, making this innovation an efficient performer. The tiny size and light weight makes the system useful for commercial and industrial applications including surface damage inspections, range measurements, and 3D imaging.
Xu, Jing; Gutierrez, Roman
Influence of space-time speckle effect on the image quality in a synthetic aperture imaging ladar
Temporally and spatially varying speckle effect arises as a consequence of the frequency modulation chirped laser signal employed in synthetic aperture imaging ladar (SAIL).A variety of reconstructed images degraded by laser speckle effect have been reported. In this paper, space-time speckle effects and their influence on imaging based on the SAIL system in a far-field diffraction region are systematically studied. The first half of this paper provides the theoretical analyses of the 2D data acquisition with speckle effect in SAIL. Numerical simulations of the temporally varying speckle pattern, the integrated speckle field over a receiving antenna and their influence to the image quality for SAIL are obtained in the remaining of the paper. Our results will be valuable for further studies on the suppression of speckle effect in SAILs.
Xu, Qian; Zhou, Yu; Sun, Jianfeng; Sun, Zhiwei; Ma, Xiaoping; Liu, Liren
The Army Research Laboratory is developing scannerless ladar systems for smart munition and reconnaissance applications. Here we report on progress attained over the past year related to the construction of a 32x32 pixel ladar. The 32x32 pixel architecture achieves ranging based on a frequency modulation/continuous wave (FM/cw) technique implemented by directly amplitude modulating a near-IR diode laser transmitter with a radio frequency (rf) subcarrier that is linearly frequency modulated. The diode's output is collected and projected to form an illumination field in the downrange image area. The returned signal is focused onto an array of metal-semiconductor-metal (MSM) detectors where it is detected and mixed with a delayed replica of the laser modulation signal that modulates the responsivity of each detector. The output of each detector is an intermediate frequency (IF) signal (a product of the mixing process) whose frequency is proportional to the target range. This IF signal is continuously sampled over each period of the rf modulation. Following this, a N channel signal processor based-on field-programmable gate arrays calculates the discrete Fourier transform over the IF waveform in each pixel to establish the ranges to all the scatterers and their respective amplitudes. Over the past year, we have built one and two-dimensional self-mixing MSM detector arrays at .8 and 1.55 micrometers , designed and built circuit boards for reading data out of a 32x32 pixel array, and designed an N channel FPGA signal processor for high-speed formation of range gates. In this paper we report on the development and performance of these components and the results of system tests conducted in the laboratory.
Stann, Barry L.; Abou-Auf, Ahmed; Aliberti, Keith; Giza, Mark M.; Ovrebo, Greg; Ruff, William C.; Simon, Deborah R.; Stead, Michael R.
Object Based Video Retrieval Using SIFT
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Full Text Available A method for retrieving video containing a particular object, a single image of the object is given as a query. The local invariant features are obtained for all frames in a sequence and tracked throughout the shot to extract stable features. In Video Retrieval system, each video that is stored in the database has its features extracted and compared to the features of the query image. Proposed work is to retrieve video from the database by giving query as an object. Video is firstly converted into frames, these frames are then segmented and an object is separated from the image. Then features are extracted from object image by using SIFT features. Features of the video database obtained by the segmentation and feature extraction using SIFT feature are matched by Nearest Neighbor Search (NNS.
Neetesh Gupta, Shiv K Sahu
We describe the technical approach, component development, and test results of a line imager laser radar (ladar) being developed at the Army Research Laboratory (ARL) for smart munition applications. We obtain range information using a frequency modulation/continuous wave (FM/cw) technique implemented by directly amplitude modulating a near-IR diode laser transmitter with a radio frequency (rf) subcarrier that is linearly frequency modulated. The diode's output is collimated and projected to form a line illumination in the downrange image area. The returned signal is focused onto a line array of metal-semiconductor-metal (MSM) detectors where it is detected and mixed with a delayed replica of the laser modulation signal that modulates the responsivity of each detector. The output of each detector is an intermediate frequency (IF) signal (a product of the mixing process) whose frequency is proportional to the target range. This IF signal is continuously sampled over each period of the rf modulation. Following this, a N-channel signal processor based on field- programmable gate arrays (FPGA) calculates the discrete Fourier transform over the IF waveform in each pixel to establish the ranges to all the scatterers and their respective amplitudes. Over the past year, we constructed the fundamental building blocks of this ladar, which include a 3.5-W line illuminator, a wideband linear FM chirp modulator, a N-pixel MSM detector line array, and a N-channel FPGA signal processor. In this paper we report on the development and performance of each building block and the results of system tests conducted in the laboratory.
Stann, Barry L.; Abou-Auf, Ahmed; Ruff, William C.; Robinson, Dale; Liss, Brian; Potter, William; Sarama, Scott D.; Giza, Mark M.; Simon, Deborah R.; Frankel, Scott; Sztankay, Zoltan G.
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Full Text Available The goal of this study is to build a system that detects and classifies the bike objects amidst background clutter and mild occlusion. This study addresses the issues to classify objects of real world images containing side views of the bike with cluttered background with that of non-bike images with natural scenes. The threshold technique with background subtraction is used to segment the background region to extract the object of interest. The background segmented image with region of interest is divided into equal sized blocks of sub-images. The spectral texture features are extracted from each sub block. The features of the objects are fed to the back-propagation neural classifier. Thus the performance of the neural classifier is compared with various categories of block size. Quantitative evaluation shows improved results. A critical evaluation of present approach under the proposed standards is presented
K. Prakash
Geiger-mode avalanche photodiode focal plane arrays for three-dimensional imaging LADAR
We report on the development of focal plane arrays (FPAs) employing two-dimensional arrays of InGaAsP-based Geiger-mode avalanche photodiodes (GmAPDs). These FPAs incorporate InP/InGaAs(P) Geiger-mode avalanche photodiodes (GmAPDs) to create pixels that detect single photons at shortwave infrared wavelengths with high efficiency and low dark count rates. GmAPD arrays are hybridized to CMOS read-out integrated circuits (ROICs) that enable independent laser radar (LADAR) time-of-flight measurements for each pixel, providing three-dimensional image data at frame rates approaching 200 kHz. Microlens arrays are used to maintain high fill factor of greater than 70%. We present full-array performance maps for two different types of sensors optimized for operation at 1.06 ?m and 1.55 ?m, respectively. For the 1.06 ?m FPAs, overall photon detection efficiency of >40% is achieved at <20 kHz dark count rates with modest cooling to ~250 K using integrated thermoelectric coolers. We also describe the first evalution of these FPAs when multi-photon pulses are incident on single pixels. The effective detection efficiency for multi-photon pulses shows excellent agreement with predictions based on Poisson statistics. We also characterize the crosstalk as a function of pulse mean photon number. Relative to the intrinsic crosstalk contribution from hot carrier luminescence that occurs during avalanche current flows resulting from single incident photons, we find a modest rise in crosstalk for multi-photon incident pulses that can be accurately explained by direct optical scattering.
Itzler, Mark A.; Entwistle, Mark; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir; Zalud, Peter F.; Senko, Tom; Tower, John; Ferraro, Joseph
Object Recognition Based on Dual Tree Complex Wavelet Transform
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Full Text Available Automated recognition of objects from images plays an important role in many computer vision systems such as robot navigation, object manipulation and content based image retrieval. In this study, an approach for object recognition based on Dual Tree Complex Wavelet Transform (DTCWT is proposed. The proposed approach attempts to extract the detailed information of objects from the multi scale representation by DTCWT. The proposed system is tested on Columbia Object Image Library (COIL-100. All the objects are considered for the classification based on nearest neighbor classifier. The results show that the maximum recognition accuracy achieved by the proposed approach is 97.03%.
S. Elakkiya
Object -based learning method in civil engineering
Digital Repository Infrastructure Vision for European Research (DRIVER)
Many researchers have developed learning methods that can be used in different situations and conditions, which has led to numerous theories and associated applications. Traditionally, most instruction occurs in a formal classroom setting; however, various types of computer-based instruction that can be delivered anytime, anywhere, and at a lower cost, are possible these days with the help of technology. In addition, as industry moves from traditional craft-based operations to more sophistica...
Lee, Joo Hyoung
Detection of object-based manipulation by the statistical features of object contour.
Object-based manipulations, such as adding or removing objects for digital video, are usually malicious forgery operations. Compared with the conventional double MPEG compression or frame-based tampering, it makes more sense to detect these object-based manipulations because they might directly affect our understanding towards the video content. In this paper, a passive video forensics scheme is proposed for object-based forgery operations. After extracting the adjustable width areas around object boundary, several statistical features such as the moment features of detailed wavelet coefficients and the average gradient of each colour channel are obtained and input into support vector machine (SVM) as feature vectors for the classification of natural objects and forged ones. Experimental results on several videos sequence with static background show that the proposed approach can achieve an accuracy of correct detection from 70% to 95%. PMID:24529789
Richao, Chen; Gaobo, Yang; Ningbo, Zhu
Agents as objects with knowledge base state
Advances in computer technology in general and computer networks in particular have significantly affected the requirements of modern applications, where the need to operate in decentralised environments is of primary importance. The conceptual models of the applications are also becoming complex and semantically rich.A promising technology towards the design and development of systems of such domains is agent based systems. Agents, having a knowledge component, act and interact with other agents and information sources in order to achieve some goals. Platforms intended for supporting the deve
Skarmeas, Nikolaos
Tracking target objects orbiting earth using satellite-based telescopes
Energy Technology Data Exchange (ETDEWEB)
A system for tracking objects that are in earth orbit via a constellation or network of satellites having imaging devices is provided. An object tracking system includes a ground controller and, for each satellite in the constellation, an onboard controller. The ground controller receives ephemeris information for a target object and directs that ephemeris information be transmitted to the satellites. Each onboard controller receives ephemeris information for a target object, collects images of the target object based on the expected location of the target object at an expected time, identifies actual locations of the target object from the collected images, and identifies a next expected location at a next expected time based on the identified actual locations of the target object. The onboard controller processes the collected image to identify the actual location of the target object and transmits the actual location information to the ground controller.
De Vries, Willem H; Olivier, Scot S; Pertica, Alexander J
Cramer-Rao lower bound on range error for LADARs with Geiger-mode avalanche photodiodes.
The Cramer-Rao lower bound (CRLB) on range error is calculated for laser detection and ranging (LADAR) systems using Geiger-mode avalanche photodiodes (GMAPDs) to detect reflected laser pulses. For the cases considered, the GMAPD range error CRLB is greater than the CRLB for a photon-counting device. It is also shown that the GMAPD range error CRLB is minimized when the mean energy in the received laser pulse is finite. Given typical LADAR system parameters, a Gaussian-envelope received pulse, and a noise detection rate of less than 4 MHz, the GMAPD range error CRLB is minimized when the quantum efficiency times the mean number of received laser pulse photons is between 2.2 and 2.3. PMID:20733630
Johnson, Steven E
Optical image reconstruction using an astigmatic lens for synthetic-aperture imaging ladar
An optical processor for synthetic-aperture imaging ladar (SAIL) utilizing one astigmatic lens is proposed. The processor comprises two structures of transmitting and reflecting. The imaging process is mathematically analyzed using the unified data-collection equation of side-looking and down-looking SAILs. Results show that the astigmatic lens can be replaced with a cylindrical lens on certain conditions. To verify this concept, laboratory experiment is conducted, the imaging result of data collected from one SAIL demonstrator is given.
Sun, Zhiwei; Hou, Peipei; Zhi, Yanan; Sun, Jianfeng; Zhou, Yu; Xu, Qian; Liu, Liren
Learning Distance Functions for Exemplar-Based Object Recognition.
This thesis investigates an exemplar-based approach to object recognition that learns, on an image-by-image basis, the relative importance of patch-based features for determining similarity. We borrow the idea of 'family resemblances' from Wittgenstein's ...
A. L. Frome
Application of Moving Object Tracking Based on Kalman Filter Algorithm
Directory of Open Access Journals (Sweden)
Full Text Available The moving object module matching method base on Kalman Filter (KF algorithm which proposed to solve the problem of traditional moving object matching method's, that fault of huge searching range and weakness in real-time processing. Relative to traditional module matching method, the method mentioned here effectively improved the speed and the accuracy of object tracking. This method has tripled the object matching speed of traditional tracking method.
Xiao Zhansheng
Petri Net Based Spatio-temporal Relationships for Moving Objects
Digital Repository Infrastructure Vision for European Research (DRIVER)
This study presents the spatio-temporal constraint relationships of moving objects by employing Petri Net technology. The spatial constraints of moving objects are first presented in this study based on the V4I[1] theory, then the temporal constraints for moving objects are presented by applying this theory to temporal aspect. With the proposing of Moving Object Petri Net (MOPN) and Spatial Constraint Petri Net (SCPN) in this study, the spatio-temporal constraint relationships of moving objec...
Yong-shan Liu; Zhong-xiao Hao
A semantic description of learning objects based on an ontology
Digital Repository Infrastructure Vision for European Research (DRIVER)
The number of learning objects available on the Internet has significantly grown these last years and the problem of indexing and searching these learning objects is becoming crucial. Standards and norms of educative metadata such as LOM and SCORM have been proposed to handle this problem but in our opinion these proposals are not a satisfactory solution. In this paper, we propose to extend these standards with a semantic description of learning objects based on an ontology. A learning object...
Duitama, John-freddy; Defude, Bruno; Bouzeghoub, Amel; Lecocq, Claire
Fast Moving Object Segmentation Based On Active Contours
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Full Text Available Active contour method is widely used in the image processing field. Recently, it has been used in object segmentation and has attracted great attention. However, most of the existing object segmentation methods based on active contours are complex and time-consuming. They cannot be used in some real-time applications. Hence, in this paper, a fast and efficient moving object segmentation algorithm based on an improved active contour model is proposed. Firstly, moving regions are detected based on frame differences. Then their boundaries are extracted and used as the coarse objects' contours for the next segmentation based on active contour model. Thirdly, an improved active contour method is introduced to extract the final objects with the coarse objects' contours as initial values. Experiments are implemented and the results show that the proposed algorithm can segment and track moving targets quickly and correctly.
Huimin Zhang
Extraction and classification of vehicles in LADAR imagery
The work presented in this paper is based on a dataset recorded with an airborne sensor. It comprises targets like M-60, M-47, M-113, bridge layers, tank retrievers, and trucks in various types of scenes. The background-object segmentation consists of first estimating the ground level everywhere in the scene, and then for each sample simply subtracting the measured height and ground level height. No assumptions concerning flat terrain etc. are made. Samples with height above ground level higher than a certain threshold are clustered by utilizing a straightforward agglomerative clustering algorithm. Around each cluster the bounding box with minimum volume is determined. Based on these bounding boxes, too small as well as too large clusters can easily be removed. However, vehicle-sized clutter will not be removed. Clutter detection is based on estimating the normal vector for a plane approximation around each sample. This approach is based on the fact that the surface normals of a vehicle is more "modulo 90°" distributed than clutter. The aim of the classification has been to classify main battle tanks (MBTs) Two types of algorithms have been studied, one based on Dempster Shafer fusion theory, and one model based. Our dataset comprises clusters of 269 vehicles (among them 131 MBTs), and 253 clutter objects (i.e. in practice vehiclesized bushes). The experiments we have carried out show that the segmentation extracts all vehicles, the clutter detection removes 90% of the clutter, and the classification finds more than 95% of the MBTs as well as removes half of the remaining clutter.
Palm, Hans C.; Haavardsholm, Trym V.; Ajer, Halvor; Jensen, Cathrine V.
DEFF Research Database (Denmark)
OBJECTIVE To understand core curriculum design and involvement of stakeholders.METHODS Twelve homogeneous focus group interviews with a total of 88 students, house officers, seniordoctors and nurses concerning an undergraduate emergency medicine curriculum. Following content coding of transcripts, we analysed by condensation, categorisation and qualitative content analyses.RESULTS The focus group participants gave a range of reasons for defining objectives or outcomes. They found their involvement in the process essential.Their argumentation and beliefs differed significantly, revealing 2 opposite perspectives: objectives as context-free theory-based rules versus objectives as personal practice-based guidelines. The students favoured theory-based objectives, which should be defined by experts conclusively as minimum levels and checklists. The senior doctors preferred practice-based objectives, which should be decided in a collaborative, local, continuous process, and should be expressed as ideals and expectations. The house officers held both perspectives. Adding to complexity, participants also interpreted competence inconsistently and mixed concepts such as knowledge, observation, supervision, experience and expertise.DISCUSSION Participating novices' perspectives on objectives differed completely from expertise level participants. These differences in perspectives should not be underestimated, as they can lead easily to misunderstandings among stakeholders, or between stakeholders, educational leaders and curriculum designers. We recommend that concepts are discussed with stakeholders in order to reach a common understanding and point of departure for discussing outcomes. Differences in perspectives, in our opinion, need to be recognised, respected and incorporated into the curriculum design process.
MŘrcke, Anne Mette; Wichmann-Hansen, Gitte
A semantic description of learning objects based on an ontology
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Full Text Available The number of learning objects available on the Internet has significantly grown these last years and the problem of indexing and searching these learning objects is becoming crucial. Standards and norms of educative metadata such as LOM and SCORM have been proposed to handle this problem but in our opinion these proposals are not a satisfactory solution. In this paper, we propose to extend these standards with a semantic description of learning objects based on an ontology. A learning object is described by prerequisites, a content and an acquisition function. This allows defining powerful search tools and improves reusing. For reusing, we propose to define new learning objects by assembling existing objects. Assembling is specified by a composition graph composed by learning objects and composition operators (sequence, parallel, and alternative. In order to improve flexibility, we have introduced intentional objects. An intentional object is defined by a composition graph where (at least one object has been replaced by an intentional query on the learning object repository. This model is used during the adaptive process of a learning object for a specific learner. We define a notion of quality on learning objects which mainly reflects their ability to reuse. This quality may be evaluated a posteriori using metrics on objects or controlled a priori using a type system. Our model has been implemented with Sesame, a RDF database which support SeRQL a powerful query language for RDF and RDFS.
John-Freddy DUITAMA
Surveillance Video Retrieval Based on Moving Objects Detection
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Full Text Available In this article, we proposed a surveillance video retrieval method based on detection of moving objects and researched on key problems in surveillance video, such as moving objects detection, video segmentation, selection and description of key frames, and measurement of feature similarities. We detected moving objects via movement detection algorithm based on codebook, and realized automatic segmentation of surveillance video via calculating present Frame Movement Amount based on the foreground and background information in movement detection. After the above process, we proposed a video retrieval method based on the combination of SIFT and color feature.
Xuan Huang
Petri Net Based Spatio-temporal Relationships for Moving Objects
Directory of Open Access Journals (Sweden)
Full Text Available This study presents the spatio-temporal constraint relationships of moving objects by employing Petri Net technology. The spatial constraints of moving objects are first presented in this study based on the V4I[1] theory, then the temporal constraints for moving objects are presented by applying this theory to temporal aspect. With the proposing of Moving Object Petri Net (MOPN and Spatial Constraint Petri Net (SCPN in this study, the spatio-temporal constraint relationships of moving objects are presented
Yong-shan Liu
Block/object-based algorithm for estimating true motion fields
A hybrid algorithm for estimating true motion fields is proposed in this paper. This algorithm consists of three steps: block-based initial motion estimation, image segmentation, and wrong motion vector correction based on objects. The hierarchical block-matching algorithms are improved for the initial motion estimation. The improved algorithm uses an adaptive technique to propagate motion vectors between hierarchical levels. It produces accurate motion field everywhere, except in the areas of motion occlusion. In order to correct wrong motion vectors in the areas of motion occlusion, the current image is segmented into objects and an object-based method is proposed to process the estimated motion fields. With the object-based method, wrong motion vectors are detected by approximating the estimated motion field in each object with a motion model, and are corrected using an object-adaptive interpolator. The object-adaptive interpolator is also used to increase the density of the motion field. Experimental results show that the improved hierarchical block-matching algorithm outperforms the conventional hierarchical block- matching algorithms. The proposed algorithm results in dense motion fields that are smooth within every object, discontinuous between objects of different motion, and very close to the true motion fields.
Wang, Demin; Lauzon, Daniel
Feature-Level based Video Fusion for Object Detection
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Full Text Available Fusion of three-dimensional data from multiple sensors gained momentum, especially in applications pertaining to surveillance, when promising results were obtained in moving object detection. Several approaches to video fusion of visual and infrared data have been proposed in recent literature. They mainly comprise of pixel based methodologies. Surveillance is a major application of video fusion and night-time object detection is one of most important issues in automatic video surveillance. In this paper we analyze the suitability of a feature-level based video fusion technique that overcomes the drawback of pixel-based fusion techniques for object detection.
Anjali Malviya
Context based Coding of Quantized Alpha Planes for Video Objects
DEFF Research Database (Denmark)
In object based video, each frame is a composition of objects that are coded separately. The composition is performed through the alpha plane that represents the transparency of the object. We present an alternative to MPEG-4 for coding of alpha planes that considers their specific properties. Comparisons in terms of rate and distortion are provided, showing that the proposed coding scheme for still alpha planes is better than the algorithms for I-frames used in MPEG-4.
Aghito, Shankar Manuel; Forchhammer, SŘren
a New Object Based Method for Automated Extraction of Urban Objects from Airborne Sensors Data
The classification of urban objects such as buildings, trees and roads from airborne sensors data is an essential step in numerous mapping and modelling applications. The automation of this step is greatly needed as the manual processing is costly and time consuming. The increasing availability of airborne sensors data such as aerial imagery and LIDAR data offers new opportunities to develop more robust approaches for automatic classification. These approaches should integrate these data sources that have different characteristics to exceed the accuracy achieved using any individual data source. The proposed approach presented in this paper fuses the aerial images data with single return LIDAR data to extract buildings and trees for an urban area. Object based analysis is adopted to segment the entire DSM data into objects based on height variation. These objects are preliminarily classified into buildings, trees, and ground. This primary classification is used to compute the height to ground for each object to help improve the accuracy of the second phase of classification. The overlapping perspective aerial images are used to build an ortho-photo to derive a vegetation index value for each object. The second phase of classification is performed based on the height to ground and the vegetation index of each object. The proposed approach has been tested using three areas in the centre of the city of Vaihingen provided by ISPRS test project on urban classification and 3D building reconstruction. These areas have historic buildings having rather complex shapes, few high-rising residential buildings that are surrounded by trees, and a purely residential area with small detached houses. The results of the proposed approach are presented based on a reference solution for evaluation purposes. The classification evaluation exhibits highly successful classification results of buildings class. The proposed approach follows the exact boundary of trees based on LIDAR data which provide above average classification results for the trees when compared to the assumed ideal circular shaped trees in the reference data.
Moussa, A.; El-Sheimy, N.
A proto-object-based computational model for visual saliency.
State-of-the-art bottom-up saliency models often assign high saliency values at or near high-contrast edges, whereas people tend to look within the regions delineated by those edges, namely the objects. To resolve this inconsistency, in this work we estimate saliency at the level of coherent image regions. According to object-based attention theory, the human brain groups similar pixels into coherent regions, which are called proto-objects. The saliency of these proto-objects is estimated and incorporated together. As usual, attention is given to the most salient image regions. In this paper we employ state-of-the-art computer vision techniques to implement a proto-object-based model for visual attention. Particularly, a hierarchical image segmentation algorithm is used to extract proto-objects. The two most powerful ways to estimate saliency, rarity-based and contrast-based saliency, are generalized to assess the saliency at the proto-object level. The rarity-based saliency assesses if the proto-object contains rare or outstanding details. The contrast-based saliency estimates how much the proto-object differs from the surroundings. However, not all image regions with high contrast to the surroundings attract human attention. We take this into account by distinguishing between external and internal contrast-based saliency. Where the external contrast-based saliency estimates the difference between the proto-object and the rest of the image, the internal contrast-based saliency estimates the complexity of the proto-object itself. We evaluate the performance of the proposed method and its components on two challenging eye-fixation datasets (Judd, Ehinger, Durand, & Torralba, 2009; Subramanian, Katti, Sebe, Kankanhalli, & Chua, 2010). The results show the importance of rarity-based and both external and internal contrast-based saliency in fixation prediction. Moreover, the comparison with state-of-the-art computational models for visual saliency demonstrates the advantage of proto-objects as units of analysis. PMID:24281243
Yanulevskaya, Victoria; Uijlings, Jasper; Geusebroek, Jan-Mark; Sebe, Nicu; Smeulders, Arnold
RFID and IP Based Object Identification in Ubiquitous Networking
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Full Text Available Ubiquitous networking is an integrated part of future networking technology that can provide capabilities for connecting all of objects (computers, human, PDAs, cell phones etc. in future network. It has to meet the challenge of seamless connection for communication between human and objects in internet infrastructure. Unique object identification is very much important to make the communication between objects possible. RFID tag can be used as unique identifier to identify a physical object. Radio Frequency Identification (RFID is a technology used for object identification of system. Internet Protocol (IPaddress is used to provide logical identity to find the location of object for communication. In this paper, IP based RFID architecture for unique identification and tracking of object by considering mobility isproposed. RFID Agent (RA is used to generate IP address based on RFID tags. In proposed solution, RFID-IP mapping is used to identify and track the location of object as RFID Tag ID can be used to generate unique identifier and IP can be used to find the location. RFID deployment is cost effective and IP is being used as current internet structure. In this way RFID-IP mapping provides better solution for object identification in ubiquitous networking environment.
Nisha Vaghela
The IUE data base: Homogenizing the IUE object nomenclature
The IUE project started to homogenize the object nomenclature in the IUE data base. Due to the absence of an official IAU nomenclature hierarchy and in view of the increasing confusion in IUE (and, in general, astronomical) object identifications, the IUE project adopted its own nomenclature hierarchy. The scheme and problems encountered in establishing it are described.
Barylak, Michael; Wamsteker, Willem; Schmitz, Marion
Virtual Conference Audio Reconstruction Based on Spatial Object
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Full Text Available This paper proposed a virtual conference audio reconstruction model based on spatial audio object. The aim of the model is to enhance the realistic experience of virtual conference. Firstly, the conference audio synthesis method is given according the principle of the virtual conference. Then the spatial audio parameters interaural level difference (ILD are used to reconstruct the spatial sound field for each listener based on the theory of spatial audio object coding.
Bo Hang
Autocorrelation based reconstruction of two-dimensional binary objects
International Nuclear Information System (INIS)
A method for reconstructing two-dimensional binary objects from its autocorrelation function is discussed. The objects consist of a finite set of identical elements. The reconstruction algorithm is based on the concept of class of element pairs, defined as the set of element pairs with the same separation vector. This concept allows to solve the redundancy introduced by the element pairs of each class. It is also shown that different objects, consisting of an equal number of elements and the same classes of pairs, provide Fraunhofer diffraction patterns with identical intensity distributions. However, the method predicts all the possible objects that produce the same Fraunhofer pattern. (author)
A Secure and Robust Object-Based Video Authentication System
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Full Text Available An object-based video authentication system, which combines watermarking, error correction coding (ECC, and digital signature techniques, is presented for protecting the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART coefficients is selected as the feature to represent the video object and the background, respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust authentication watermark. This content-based, semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. In watermark embedding and extraction, groups of discrete Fourier transform (DFT coefficients are randomly selected, and their energy relationships are employed to hide and extract the watermark. The experimental results demonstrate that our system is robust to MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, and scaling while securely preventing malicious object modifications. The proposed solution can be further incorporated into public key infrastructure (PKI.
He Dajun
Optical Aperture Synthesis Object's Information Extracting Based on Wavelet Denoising
International Nuclear Information System (INIS)
Wavelet denoising is studied to improve OAS(optical aperture synthesis) object's Fourier information extracting. Translation invariance wavelet denoising based on Donoho wavelet soft threshold denoising is researched to remove Pseudo-Gibbs in wavelet soft threshold image. OAS object's information extracting based on translation invariance wavelet denoising is studied. The study shows that wavelet threshold denoising can improve the precision and the repetition of object's information extracting from interferogram, and the translation invariance wavelet denoising information extracting is better than soft threshold wavelet denoising information extracting
Stereovision-Based Object Segmentation for Automotive Applications
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Full Text Available Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentation method has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map (X-Z plane and in layered images (X-Y planes. The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.
Chris Thompson
A ladar (laser radar) sensor designed around a pulsed passively Q-switched CO2 laser, capable of a efficient and rapid scans with a narrow beam over a wide field of view, is considered for surveillance and tracking applications in space. The output is a train of narrow pulses with a controllable pulse repetition rate. A resonant vibrating mirror in back of a classical Gregorian telescope, and a plane pointing mirror in front for beam steering, are used in scanning. Scan pulse sequences are described and illustrated. The 10.6 micron ladar set is under consideration as baseline sensor for various space rendezvous and docking applications.
Levinson, S.; Adelman, S.; Lowrey, D. O.
Vector ordinal optimization based multi-objective transmission planning
International Nuclear Information System (INIS)
The deregulation of the power industry has resulted in a restructured industry. The integrated power industry has been separated into generation companies, transmission company and distribution companies. Each individual market participant has its own goal of maximizing its profit in power system planning and power system operation. In this paper, the vector ordinal optimization (VOO) theory was applied to solve the multi-objective transmission expansion planning (TEP) problems. The weight-summation of multiple objectives was considered as a single objective. In order to reflect the interests of different market participants and the social benefit, the authors used the Transmission Economic Assessment Methodology (TEAM) to formulate the multi-objective TEP. The VOO solution algorithm was presented and tested based on the TEAM model. Numerical examples were presented to test the proposed VOO based solution algorithm. The 4 indices of the transmission economic assessment methodology were used as the 4 objectives for transmission planning. VOO uses crude models to estimate the indices of the TEAM base multi-objective optimization problem to determine a select subset of schemes to simulate and find solutions which have been termed as good enough. The calculation burden was reduced significantly by using this method. Test results on the modified IEEE 14-bus system show that the VOO is efficient and practical for solving multi-objective TEP problems. The test results show that the proposed VOO approach can find good enough solutions in a short time with less computational burden. 11 refs., 5 tabs., 3 figs., 1 appendix.
Research on moving object detection based on frog's eyes
On the basis of object's information processing mechanism with frog's eyes, this paper discussed a bionic detection technology which suitable for object's information processing based on frog's vision. First, the bionics detection theory by imitating frog vision is established, it is an parallel processing mechanism which including pick-up and pretreatment of object's information, parallel separating of digital image, parallel processing, and information synthesis. The computer vision detection system is described to detect moving objects which has special color, special shape, the experiment indicates that it can scheme out the detecting result in the certain interfered background can be detected. A moving objects detection electro-model by imitating biologic vision based on frog's eyes is established, the video simulative signal is digital firstly in this system, then the digital signal is parallel separated by FPGA. IN the parallel processing, the video information can be caught, processed and displayed in the same time, the information fusion is taken by DSP HPI ports, in order to transmit the data which processed by DSP. This system can watch the bigger visual field and get higher image resolution than ordinary monitor systems. In summary, simulative experiments for edge detection of moving object with canny algorithm based on this system indicate that this system can detect the edge of moving objects in real time, the feasibility of bionic model was fully demonstrated in the engineering system, and it laid a solid foundation for the future study of detection technology by imitating biologic vision.
Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan
Distinct mechanisms subserve location- and object-based visual attention.
Visual attention can be allocated to either a location or an object, named location- or object-based attention, respectively. Despite the burgeoning evidence in support of the existence of two kinds of attention, little is known about their underlying mechanisms in terms of whether they are achieved by enhancing signal strength or excluding external noises. We adopted the noise-masking paradigm in conjunction with the double-rectangle method to probe the mechanisms of location-based attention and object-based attention. Two rectangles were shown, and one end of one rectangle was cued, followed by the target appearing at (a) the cued location; (b) the uncued end of the cued rectangle; and (c) the equal-distant end of the uncued rectangle. Observers were required to detect the target that was superimposed at different levels of noise contrast. We explored how attention affects performance by assessing the threshold versus external noise contrast (TvC) functions and fitted them with a divisive inhibition model. Results show that location-based attention - lower threshold at cued location than at uncued location - was observed at all noise levels, a signature of signal enhancement. However, object-based attention - lower threshold at the uncued end of the cued than at the uncued rectangle - was found only in high-noise conditions, a signature of noise exclusion. Findings here shed a new insight into the current theories of object-based attention. PMID:24904472
Chou, Wei-Lun; Yeh, Su-Ling; Chen, Chien-Chung
Distinct Mechanisms Subserve Location- and Object-Based Visual Attention
Directory of Open Access Journals (Sweden)
Full Text Available Visual attention can be allocated to either a location or an object, named location- or object-based attention, respectively. Despite the burgeoning evidence in support of the existence of two kinds of attention, little is known about their underlying mechanisms in terms of whether they are achieved by enhancing signal strength or excluding external noises. We adopted the noise-masking paradigm in conjunction with the double-rectangle method to probe the mechanisms of location-based attention and object-based attention. Two rectangles were shown, and one end of one rectangle was cued, followed by the target appearing at (a the cued location; (b the uncued end of the cued rectangle; and (c the equal-distant end of the uncued rectangle. Observers were required to detect the target that was superimposed at different levels of noise contrast. We explored how attention affects performance by assessing the threshold versus external noise contrast (TvC functions and fitted them with a divisive inhibition model. Results show that location-based attention—lower threshold at cued location than at uncued location—was observed at all noise levels, a signature of signal enhancement. However, object-based attention—lower threshold at the uncued end of the cued than at the uncued rectangle—was found only in high-noise conditions, a signature of noise exclusion. Findings here shed a new insight into the current theories of object-based attention.
Summarization-based image resizing by intelligent object carving.
Image resizing can be more effectively achieved with a better understanding of image semantics. In this paper, similar patterns that exist in many real-world images are analyzed. By interactively detecting similar objects in an image, the image content can be summarized rather than simply distorted or cropped. This method enables the manipulation of image pixels or patches as well as semantic objects in the scene during image resizing process. Given the special nature of similar objects in a general image, the integration of a novel object carving (OC) operator with the multi-operator framework is proposed for summarizing similar objects. The object removal sequence in the summarization strategy directly affects resizing quality. The method by which to evaluate the visual importance of the object as well as to optimally select the candidates for object carving is demonstrated. To achieve practical resizing applications for general images, a template matching-based method is developed. This method can detect similar objects even when they are of various colors, transformed in terms of perspective, or partially occluded. To validate the proposed method, comparisons with state-of-the-art resizing techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method. PMID:24201330
Dong, Weiming; Zhou, Ning; Lee, Tong-Yee; Wu, Fuzhang; Kong, Yan; Zhang, Xiaopeng
Semantic-based retrieval of cultural heritage multimedia objects
Digital Repository Infrastructure Vision for European Research (DRIVER)
Today's search interfaces typically offer keyword searches and facets for the retrieval of cultural heritage multimedia objects. Facets, however, are usually based on a static set of metadata fields. This set is often called an indexing profile. Graph-based repositories based on predicates about resources allow for more precise semantics. They offer stronger support for retrieval, and they can be adopted to almost any metadata format. Technically, those predicates may be serialized as RDF tri...
Stalmann, Kai; Wegener, Dennis; Doerr, Martin; Hill, Hermann Josef; Friesen, Natalja
An object-based methodology for knowledge representation in SGML
Energy Technology Data Exchange (ETDEWEB)
An object-based methodology for knowledge representation and its Standard Generalized Markup Language (SGML) implementation is presented. The methodology includes class, perspective domain, and event constructs for representing knowledge within an object paradigm. The perspective construct allows for representation of knowledge from multiple and varying viewpoints. The event construct allows actual use of knowledge to be represented. The SGML implementation of the methodology facilitates usability, structured, yet flexible knowledge design, and sharing and reuse of knowledge class libraries.
Kelsey, R.L. [Los Alamos National Lab., NM (United States)|New Mexico State Univ., Las Cruces, NM (United States); Hartley, R.T. [New Mexico State Univ., Las Cruces, NM (United States); Webster, R.B. [Los Alamos National Lab., NM (United States)
Nanoscale synthesis and characterization of graphene-based objects
Digital Repository Infrastructure Vision for European Research (DRIVER)
Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (ST...
Daisuke Fujita
Multi-objective Optimization using Chaos Based PSO
Directory of Open Access Journals (Sweden)
Full Text Available As a novel optimization method, chaos has gained lots of attentions and applications in the past few years. Chaos movement can go through all states unrepeated according to the rule of itself in some area. It was introduced into the optimization strategy to accelerate the optimum seeking operation in this study. A chaos based particle swarm optimization strategy was developed to solve multi-objective optimization problems. The proposed approach is validated using several benchmark test functions and metrics on evolutionary multi-objective optimization. Results demonstrate the effectiveness and efficiency of the proposed strategy and that can be considered a viable alternative to solve multi-objective optimization problems.
Weizhou Zhong
A primitive-based 3D object recognition system
An intermediate-level knowledge-based system for decomposing segmented data into three-dimensional primitives was developed to create an approximate three-dimensional description of the real world scene from a single two-dimensional perspective view. A knowledge-based approach was also developed for high-level primitive-based matching of three-dimensional objects. Both the intermediate-level decomposition and the high-level interpretation are based on the structural and relational matching; moreover, they are implemented in a frame-based environment.
Dhawan, Atam P.
Video Based Moving Object Tracking by Particle Filter
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Full Text Available Usually, the video based object tracking deal with non-stationary image stream that changes over time. Robust and Real time moving object tracking is a problematic issue in computer vision research area. Most of the existing algorithms are able to track only inpredefined and well controlled environment. Some cases, they don't consider non-linearity problem. In our paper, we develop such a system which considers color information, distance transform (DT based shape information and also nonlinearity. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. We examine the difficulties of video based tracking and step by step we analyze these issues. In our firstapproach, we develop the color based particle filter tracker that relies on the deterministic search of window, whose color content matches a reference histogram model. A simple HSV histogram-based color model is used to develop this observation system. Secondly, wedescribe a new approach for moving object tracking with particle filter by shape information. The shape similarity between a template and estimated regions in the video scene is measured by their normalized cross-correlation of distance transformed images. Our observation system of particle filter is based on shape from distance transformed edge features. Template is created instantly by selecting any object from the video scene by a rectangle. Finally, inthis paper we illustrate how our system is improved by using both these two cues with non linearity.
Md. Zahidul Islam
Novel Scheme for Object-based Embedded Image Coding
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Full Text Available Conventional EBCOT algorithm encodes an image with the same block-based coding strategy and it is difficult to acquire perceptually consistent results in practical applications. This paper presents a new scheme for object-based embedded coding aiming to enhance the flexibility and the coding efficiency. In our scheme, an original image is firstly segmented into objects, which are taken as basic coding units and are encoded independently. Then the compressed streams of all objects are further truncated and reassembled based on rate-distortion optimization principle according to given bit-rate. In our scheme, each object can be encoded independently with different strategy according to its visual interest at the encoder. At the decoder, it can be decoded and reconstructed independently and progressively. As a whole, the new scheme is more flexible and may enhance the overall subjective quality of reconstructed images. Experiments are conducted and the results show the proposed scheme can implement object-based image coding and rate control effectively.
Yuer Wang
Multi-objective Optimization Problem Based on Genetic Algorithm
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Full Text Available Target weighted multi-objective optimization genetic algorithm for solving the problem is to place all aggregated into a target objective function with parameters. In the multi-objective optimization evaluation index system, determine the weights of attributes have a pivotal position. So how to scientifically and reasonably determine the attribute weights, the results related to the multi-objective optimization reliability and validity. The first focuses on the weighted sum of the genetic algorithm, uniform design created by combining the initial population and its standardization of the objective function to create a new fitness function, we propose a dynamic allocation weighting scheme, based on the design of a new weight distribution strategy multi-objective genetic algorithm for solving multi-objective optimization problem. The algorithm can find the sparse regions of non-dominated frontier, to search for sparse areas, making the search to a more uniform distribution of non-dominated solutions, introduces a uniform crossover operator and single point crossover two kinds of hybrid composite operator, to make up for a simulated binary search capability is weak crossover defects and gives proof of convergence of the algorithm by simulation to verify the effectiveness of the algorithm.
Li Heng
Fuzzy-Rule-Based Object Identification Methodology for NAVI System
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Full Text Available We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI system. The NAVI has a single board processing system (SBPS, a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.
Yaacob Sazali
Distributed Database for Reusable Learning Object-Based System
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Full Text Available In this study, discusses enhancing the e-learning system by employing a distributed database that apply reusable learning object as one of the technologies that is applied to e-learning development. An e-learning system named e-notes is able to assist students and facilitators to interact with each other. Learning objects are used to conceptualize the learning process and offer accessibility, interoperability, adaptability, durability, reusability and granularity for the e-learning environment. This study also shows the test result of a comparison for learning objects` performance applied to a centralized database and a distributed database and the performance in reusability of learning objects. The e-notes architecture, based on a client-side application, is designed with the intention to help the student in a computer tutorial system which offers reusability attributes.
S.H.A. Hamid
Moving Objects Segmentation Based on Histogram for Video Surveillance
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Full Text Available The detection of moving object is one of the key techniques for video surveillance. In order to extract the moving object robustly in complex background, this paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes. The difference image of color distance between current image and the reference background image in YUV color space is first obtained. According to the mono-modal feature of histogram of the difference image, an adaptive clustering method based on histogram is given. With morphological filtering, the flecks of noise existed in the segmented binary image can be removed. Finally, an updating scheme for background image is introduced to follow the variation of illumination and environmental conditions. Experimental results show that the proposed approach can detect moving objects effectively from video sequences.
Jinglan Li
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Full Text Available I argue that one in particular of Crispin Wright's attempts to capture our common or intuitive concepts of objectivity, warrant, and other associated notions, relies on an ambiguity between a given constructivist reading of the concepts and at least one other, arguably more 'ordinary', version of the notions he tries to accommodate. I do this by focusing on one case in point, and concluding with a brief argument showing how this case generalises. I demonstrate why this ambiguity is unacceptable and also that its resolution undermines the aim it serves: to account for and accommodate our ordinary conception of (at least objectivity, warrant (or justification and truth.
Penelope A Rush
Criterion-Referenced Tests and Other Objective-Based Tests.
The Center for the Study of Evaluation (CSE) compiled a catalog of existing criterion referenced tests (CRT) and other objectives-based tests for use in kindergarten through grade 12. CSE consulted publishers' catalogs, bibliographies, and test lists, and conducted computer searches of the Educational Resources Information Center (ERIC) and…
Kampe, Lynnette; Walker, Clinton B.
Distributed Shared Memory Consistency Object-based Model
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Full Text Available A novel model that describes consistency in shared memory was developed, presented and discussed. The new object-based model handles errors of inaccuracy and misrepresentation in distributed shared memory process. The issue of misalignment was also covered.
Abdelfatah A. Yahya
Object based data access at the DO experiment
International Nuclear Information System (INIS)
The DO experiment at the Fermilab is currently participating in the FNAL Computing Division's Computing for Analysis Project (CAP) to investigate object based data storage and access. Following a short description of the CAP system architecture, the D0 data model is explored. A brief discussion of the method of operation of the CAP system leads into a concluding section. (author)
Archive Design Based on Planets Inspired Logical Object Model
DEFF Research Database (Denmark)
We describe a proposal for a logical data model based on preliminary work the Planets project In OAIS terms the main areas discussed are related to the introduction of a logical data model for representing the past, present and future versions of the digital object associated with the Archival Storage Package for the publications deposited by our client repositories.
Zierau, Eld
Vision-based autonomous grasping of unknown piled objects
International Nuclear Information System (INIS)
Computer vision techniques have been used to develop a vision-based grasping capability for autonomously picking and placing unknown piled objects. This work is currently being applied to the problem of hazardous waste sorting in support of the Department of Energy's Mixed Waste Operations Program
Object based data access at the D0 experiment
International Nuclear Information System (INIS)
The D OE Experiment at Fermilab is currently participating in the FNAL Computing Division's ''Computing for Analysis Project'' (CAP) to investigate object based data storage and access. Following a short description of the CAP system architecture, the D OE data model is explored. A brief discussion of the method of operation of the CAP system leads into a concluding section
The System for Objectives-Based Evaluation--Reading.
The System for Objectives-Based Evaluation--Reading (SOBE-R) is discussed. Evaluative inadequacies of standardized tests are listed, and the functions and components of the SOBE-R program are examined. Preliminary evaluations of SOBE-R are included, as are its expected accomplishments. (DLG)
Skager, Rodney
The characterization of terrestrial ecosystems using remote sensing technology has a long history with using multi-spectral imagers for vegetation classification indices, ecosystem health, and change detection. Traditional multi-band imagers are now being replaced with more advanced hyperspectral imagers, which offer finer spectral resolution and more specific characterization of terrestrial reflectances. Recently, 3- dimensional (3D) imaging technologies, such as radar interferometry and scanning laser rangers, have added a vertical dimensional to the characterization of ecosystems. The combination of hyperspectral imagery with 3D LADAR allows for detailed analysis of terrestrial biomass, health and species identification. Recognizing the need, and the technical feasibility of this type of environmental assessment, the National Research Counsel has advocated two future NASA satellite missions to measure terrestrial ecosystem health and structure, the DESDynI and HyspIRI missions. These programs will orbit synthetic aperture radar, LADAR and hyperspectral imagers. To mitigate program risk it is desirable and prudent to first demonstrate the integration of these instruments on an airborne platform. Although systems developed for separate purposes have been flown on a single aircraft, the requirements and performance of a dual sensor system has not yet been developed nor integrated as a single unit. We demonstrate a development pathway from an aircraft platform with an integrated sensor suite, using a hyperspectral imager and a laser ranger for a comprehensive remote sensing characterization of terrestrial ecology.
Swanson, A. L.; Sandor-Leahy, S.; Shepanski, J.; Wong, C.; Bracikowski, C.; Abelson, L.; Helmlinger, M.; Bauer, D.; Folkman, M.
New developments in HgCdTe APDs and LADAR receivers
Raytheon is developing NIR sensor chip assemblies (SCAs) for scanning and staring 3D LADAR systems. High sensitivity is obtained by integrating high performance detectors with gain, i.e., APDs with very low noise Readout Integrated Circuits (ROICs). Unique aspects of these designs include: independent acquisition (non-gated) of pulse returns, multiple pulse returns with both time and intensity reported to enable full 3D reconstruction of the image. Recent breakthrough in device design has resulted in HgCdTe APDs operating at 300K with essentially no excess noise to gains in excess of 100, low NEP linear mode photon counting. SCAs utilizing these high performance APDs have been integrated and demonstrated excellent spatial and range resolution enabling detailed 3D imagery both at short range and long ranges. In the following we will review progress in real-time 3D LADAR imaging receiver products in two areas: (1) scanning 256 × 4 configuration for the Multi-Mode Sensor Seeker (MMSS) program and (2) staring 256 × 256 configuration for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) lunar landing mission.
McKeag, William; Veeder, Tricia; Wang, Jinxue; Jack, Michael; Roberts, Tom; Robinson, Tom; Neisz, James; Andressen, Cliff; Rinker, Robert; Cook, T. Dean; Amzajerdian, Farzin
Robust Object Tracking Based on Adaptive Feature Selection
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Full Text Available In order to solve the video-based object tracking problem in complex dynamic scenes, a robust tracking algorithm based on adaptive feature selection was proposed in this paper. To address the poor robustness of candidates in the feature pool in the online Adaboost algorithm and the drift problem caused by the template updating, a new feature pool was built based on both color features and histogram of pyramidal gradient features. An occlusion detector is added after the tracking in the current frame to improve the reliability of the realtime updated template. Experimental results showed that the proposed algorithm has better performance against object deformation, pose transformation, illumination variance and occlusion.
Chen Dong-Yue
Multi Objective AODV Based On a Realistic Mobility Model
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Full Text Available Routing is one of the most important challenges in ad hoc network. Numerous algorithms have been presented and one of the most important of them is AODV. This algorithm like many other algorithm calculate optimum path while pays no attention to environment situations, mobility pattern and mobile nodes status. However several presented algorithm have considered this situation and presented algorithm which named environment aware or mobility based. But in them have not considered realistic movement and environment such as obstacles, pathways and realistic movement pattern of the mobile nodes. This article present new algorithm based on AODV which find optimum path based on multi objectives. These objectives have been mined from a realistic mobility model, internal status of the mobile nodes and its status in routing. In this method the objectives are optional and each node can consider a couple of them in routing. Therefore this method supports GPS less mobile nodes. Evaluation of the new method shows that considering multi objectives influence routing metrics and can improve some of them.
Hamideh Babaei
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Full Text Available This work uses data from the Spanish Tourism Demand Segments Survey (N=6900 conducted by the IESA-CSIC for Turismo Andaluz, SA. The objective of the paper is to develop a statistical segmentation or typology of Spanish tourists based on objective aspects of tourist behaviour measured in the survey including destinations visited, theme of the trip, lodging, transportation and travel group. Initial categorical data are reduced using multiple correspondence analysis and grouped through cluster analysis. Afterwards, identified segments are evaluated to analyse their tourist profiles with a view to examining sociological perspectives of tourist behaviour.
Oscar Molina Molina
Teaching object concepts for XML-based representations.
Energy Technology Data Exchange (ETDEWEB)
Students learned about object-oriented design concepts and knowledge representation through the use of a set of toy blocks. The blocks represented a limited and focused domain of knowledge and one that was physical and tangible. The blocks helped the students to better visualize, communicate, and understand the domain of knowledge as well as how to perform object decomposition. The blocks were further abstracted to an engineering design kit for water park design. This helped the students to work on techniques for abstraction and conceptualization. It also led the project from tangible exercises into software and programming exercises. Students employed XML to create object-based knowledge representations and Java to use the represented knowledge. The students developed and implemented software allowing a lay user to design and create their own water slide and then to take a simulated ride on their slide.
Kelsey, R. L. (Robert L.)
A Constrained Object Model for Configuration Based Workflow Composition
Automatic or assisted workflow composition is a field of intense research for applications to the world wide web or to business process modeling. Workflow composition is traditionally addressed in various ways, generally via theorem proving techniques. Recent research observed that building a composite workflow bears strong relationships with finite model search, and that some workflow languages can be defined as constrained object metamodels . This lead to consider the viability of applying configuration techniques to this problem, which was proven feasible. Constrained based configuration expects a constrained object model as input. The purpose of this document is to formally specify the constrained object model involved in ongoing experiments and research using the Z specification language.
Albert, P; Kleiner, M; Albert, Patrick; Henocque, Laurent; Kleiner, Mathias
Nanoscale synthesis and characterization of graphene-based objects
Energy Technology Data Exchange (ETDEWEB)
Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM) has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moire patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.
Fujita, Daisuke, E-mail: firstname.lastname@example.com [International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science 1-2-1 Sengen, Tsukuba 305-0047 (Japan)
Nanoscale synthesis and characterization of graphene-based objects
Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM) has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moiré patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.
Fujita, Daisuke
Nanoscale synthesis and characterization of graphene-based objects
International Nuclear Information System (INIS)
Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM) has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moire patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.
Nanoscale synthesis and characterization of graphene-based objects
Directory of Open Access Journals (Sweden)
Full Text Available Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moiré patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.
Daisuke Fujita
Improved Brain Tumor Detection Using Object Based Segmentation
Directory of Open Access Journals (Sweden)
Full Text Available This paper has focused on the brain tumor detection techniques. The brain tumor detection is a very important vision application in the medical field. This work has firstly presented a review on various well known techniques for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main objective of the work is to explore various techniques to detect brain tumor in an efficient way. It has been found that the most of existing methods has ignored the poor quality images like images with noise or poor brightness. Also the most of the existing work on tumor detection has neglected the use of object based segmentation. So to overcome the limitations of earlier work a new technique has been proposed in this research work. The technique has shown quite effective results than neural based tumor detection technique. The design and implementation of the proposed algorithm is done in MATLAB using image processing toolbox. The comparison has shown that the proposed technique has achieved up to 94 % accuracy which was 78 % in neural based technique. Also for high corrupted noisy images the proposed technique has shown quite effective results than the neural based tumor detection.
Harneet Kaur
A Quaternionic Wavelet Transform-based Approach for Object Recognition
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Full Text Available Recognizing the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT is presented. This transform separates the information contained in the image better than a traditional Discrete wavelet transform and provides a multiscale image analysis whose coefficients are 2D analytic, with one near-shift invariant magnitude and three phases. The two phases encode local image shifts and the third one contains texture information. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, patches are extracted over the interest points detected from the original image using Wavelet based interest point detector. Here QWT magnitude and phase features are computed for every patch. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. In order to compare the performance of local feature with global feature, the transform is applied to the entire image and the global features are derived. The performance of QWT is compared with discrete wavelet transform (DWT and dual tree discrete wavelet transform (DTDWT. Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The experimental evaluation is done using the complex Graz databases.
R. Ahila Priyadharshini
Likelihood-based CT reconstruction of objects containing known components
International Nuclear Information System (INIS)
There are many situations in medical imaging where there are known components within the imaging volume. Such is the case in diagnostic X-ray CT imaging of patients with implants, in intraoperative CT imaging where there may be surgical tools in the field, or in situations where the patient support (table or frame) or other devices are outside the (truncated) reconstruction FOV. In such scenarios it is often of great interest to image the relation between the known component and the surrounding anatomy, or to provide high-quality images at the boundary of these objects, or simply to minimize artifacts arising from such components. We propose a framework for simultaneously estimating the position and orientation of a known component and the surrounding volume. Toward this end, we adopt a likelihood-based objective function with an image volume jointly parameterized by a known object, or objects, with unknown registration parameters and an unknown background attenuation volume. The objective is solved iteratively using an alternating minimization approach between the two parameter types. Because this model integrates a substantial amount of prior knowledge about the overall volume, we expect a number of advantages including the reduction of metal artifacts, potential for more sparse data acquisition (decreased time and dose), and/or improved image quality. We illustrate this approach using simulated spine CT data that contains pedicle screws placed in a vertebra, and deedicle screws placed in a vertebra, and demonstrate improved performance over traditional filtered-backprojection and penalized-likelihood reconstruction techniques. (orig.)
A CDP method in an object-based file system
Recent advances in large-capacity, low-cost storage devices have led to active research in design of large-scale storage system built from commodity devices. These storage systems are composed of thousands of storage device and require an efficient file system to provide high system bandwidth and petabyte-scale data storage. Object-based file system integrates advantage of both NAS and SAN, can be applied in above environment. Continuous data protection (CDP) is a methodology that continuously captures or tracks data modifications and stores changes independent of the primary data, enabling recovery points from any point in the past. All changes to files and file metadata are stored and managed. A CDP method in Object-based file system is presented in this thesis to improve the system reliability. Firstly, we can get detail at byte level of every write request because data protection operates at the file system level. It can consume less storage space. Secondly, every object storage server can compute the recovery strip data object independently to decrease the recovery time. Thirdly a journal-like metadata management way is introduced to provide metadata optimization for CDP.
Yao, Jie; Cao, Qiang; Li, Huaiyang
Knowledge-based simulation using object-oriented programming
Simulations have become a powerful mechanism for understanding and modeling complex phenomena. Their results have had substantial impact on a broad range of decisions in the military, government, and industry. Because of this, new techniques are continually being explored and developed to make them even more useful, understandable, extendable, and efficient. One such area of research is the application of the knowledge-based methods of artificial intelligence (AI) to the computer simulation field. The goal of knowledge-based simulation is to facilitate building simulations of greatly increased power and comprehensibility by making use of deeper knowledge about the behavior of the simulated world. One technique for representing and manipulating knowledge that has been enhanced by the AI community is object-oriented programming. Using this technique, the entities of a discrete-event simulation can be viewed as objects in an object-oriented formulation. Knowledge can be factual (i.e., attributes of an entity) or behavioral (i.e., how the entity is to behave in certain circumstances). Rome Laboratory's Advanced Simulation Environment (RASE) was developed as a research vehicle to provide an enhanced simulation development environment for building more intelligent, interactive, flexible, and realistic simulations. This capability will support current and future battle management research and provide a test of the object-oriented paradigm for use in large scale military applications.
Sidoran, Karen M.
Fission-track dating using object-based image analysis
International Nuclear Information System (INIS)
Full text: Geological dating with the help of fission track analysis is based on a time-consuming counting of the spontaneous and induced tracks in the minerals. Fission tracks are damage trails in minerals caused by fast charged particles, released in nuclear fission. In this study the 950;-method is used for fission-track dating. In order to determine the age, spontaneous tracks in the apatite and induced tracks in the muscovite external detector have to be counted. The automatic extraction and identification would not only improve the speed of track counting and eliminate the personal factor. Pixel values alone are not enough to distinguish between tracks and background. Traditional pixel based approaches are therefore inefficient for fission track counting. Image analysis based on objects, which include shape, texture and contextual information is a more promising method. A procedure for automatic object - based classification is used to extract the track objects. Resolving the individual tracks in a multi-track object is based on morphological operations. The individual track objects are skeletonized and the number of individual tracks in the object is counted by processing the skeletons. To give the right fission track age, there has to be a calibration of every single user manually counting the tracks. We calibrate the automatic approach for counting in the same way. Durango apatite standard samples are used to determine the 950;- and Z-calibration factor. The e the 950;- and Z-calibration factor. The automatic approach is useful for counting tracks in apatite standards and induced tracks in muscovite external detectors where the quality and quantities of the etched tracks is high. Muscovite detectors irradiated against glasses can also be used to determine the thermal neutron fluence, which is necessary to determine an absolute age. These images are of high quality and free of disturbing background irregularities. Here the automatic approach is a practical alternative. However for natural samples of small grain size, low track-numbers and background irregularities, the implementation is questionable. The algorithm for the automatic extraction and counting of fission tracks in standard samples of Durango Apatite and muscovite external detectors is shown to be self-consistent. (author)
Data Warehouse Requirements Analysis Framework: Business-Object Based Approach
Directory of Open Access Journals (Sweden)
Full Text Available Detailed requirements analysis plays a key role towards the design of successful Data Warehouse (DW system. The requirements analysis specifications are used as the prime input for the construction of conceptual level multidimensional data model. This paper has proposed a Business Object based requirements analysis framework for DW system which is supported with abstraction mechanism and reuse capability. It also facilitate the stepwise mapping of requirements descriptions into high level design components of graph semantic based conceptual level object oriented multidimensional data model. The proposed framework starts with the identification of the analytical requirements using business process driven approach and finally refine the requirements in further detail to map into the conceptual level DW design model using either Demand-driven of Mixed-driven approach for DW requirements analysi
Anirban Sarkar
Multi-objective evolutionary algorithm based filters for image enhancement
Multi-objective evolutionary algorithms (MOEAs) have been utilized in many fields to optimize designs and constraints using biologically inspired methods. In this research, MOEAs are used to determine more optimal DCT based filter coefficient sets in order to enhance images under various image processing attacks and functions. The filter coefficients are adapted to minimize the mean squared error and to remove noise-induced artifacts. The capabilities of the proposed enhanced image filters are demonstrated on multiple digital images.
McLauchlan, Lifford; Mehrübeo?lu, Mehrübe
A Voxel-Based Approach for Virtual Objects Relighting
Digital Repository Infrastructure Vision for European Research (DRIVER)
This paper presents a new way to insert virtual objects into real environments. It proposes an image based method to determine light interactions between the different parts of the scene. Unlike most previous works, our tech- nique does not need any manual modeling: data are extracted from multiple 2.5D high dynamic range captures of the scene. These images are obtained via a calibrated time of flight camera coupled with an ordinary webcam. Visibility infor- mation and light transport are sto...
Fouquet, Franc?ois; Farrugia, Jean-philippe; Brandel, Sylvain
DMD-based multi-object spectrograph on Galileo telescope
Next-generation infrared astronomical instrumentation for ground-based and space telescopes could be based on MOEMS programmable slit masks for multi-object spectroscopy (MOS). This astronomical technique is used extensively to investigate the formation and evolution of galaxies. We propose to develop a 2048x1080 DMD-based MOS instrument to be mounted on the Galileo telescope and called BATMAN. A two-arm instrument has been designed for providing in parallel imaging and spectroscopic capabilities. The two arms with F/4 on the DMD are mounted on a common bench, and an upper bench supports the detectors thanks to two independent hexapods. Very good optical quality on the DMD and the detectors will be reached. ROBIN, a BATMAN demonstrator, has been designed, realized and integrated. It permits to determine the instrument integration procedure, including optics and mechanics integration, alignment procedure and optical quality. First images have been obtained and measured. A DMD pattern manager has been developed in order to generate any slit mask according to the list of objects to be observed; spectra have been generated and measured. Observation strategies will be studied and demonstrated for the scientific optimization strategy over the whole FOV. BATMAN on the sky is of prime importance for characterizing the actual performance of this new family of MOS instruments, as well as investigating the operational procedures on astronomical objects. This instrument will be placed on the Telescopio Nazionale Galileo at the beginning of next year, in 2014.
Zamkotsian, Frederic; Spano, Paolo; Lanzoni, Patrick; Bon, William; Riva, Marco; Nicastro, Luciano; Molinari, Emilio; Di Marcantonio, Paolo; Zerbi, Filippo; Valenziano, Luca
Geographic Object-Based Image Analysis – Towards a new paradigm
The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the 'per-pixel paradigm' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm. PMID:24623958
Blaschke, Thomas; Hay, Geoffrey J.; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk
WOMBAT: sWift Objects for Mhd BAsed on Tvd
WOMBAT (sWift Objects for Mhd BAsed on Tvd) is an astrophysical fluid code that is an implementation of a non-relativistic MHD TVD scheme; an extension for relativistic MHD has been added. The code operates on 1, 2, and 3D Eulerian meshes (cartesian and cylindrical coordinates) with magnetic field divergence restriction controlled by a constrained transport (CT) scheme. The user can tune code performance to a given processor based on chip cache sizes. Proper settings yield significant speed-ups due to efficient cache reuse.
Mendygral, Peter; Porter, David; Edmon, Paul; Delgado, Jennifer
Object Recognition using Feature- and Color-Based Methods
An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.
Duong, Tuan; Duong, Vu; Stubberud, Allen
Analysis of manufacturing based on object oriented discrete event simulation
Directory of Open Access Journals (Sweden)
Full Text Available This paper describes SIMMEK, a computer-based tool for performing analysis of manufacturing systems, developed at the Production Engineering Laboratory, NTH-SINTEF. Its main use will be in analysis of job shop type of manufacturing. But certain facilities make it suitable for FMS as well as a production line manufacturing. This type of simulation is very useful in analysis of any types of changes that occur in a manufacturing system. These changes may be investments in new machines or equipment, a change in layout, a change in product mix, use of late shifts, etc. The effects these changes have on for instance the throughput, the amount of VIP, the costs or the net profit, can be analysed. And this can be done before the changes are made, and without disturbing the real system. Simulation takes into consideration, unlike other tools for analysis of manufacturing systems, uncertainty in arrival rates, process and operation times, and machine availability. It also shows the interaction effects a job which is late in one machine, has on the remaining machines in its route through the layout. It is these effects that cause every production plan not to be fulfilled completely. SIMMEK is based on discrete event simulation, and the modeling environment is object oriented. The object oriented models are transformed by an object linker into data structures executable by the simulation kernel. The processes of the entity objects, i.e. the products, are broken down to events and put into an event list. The user friendly graphical modeling environment makes it possible for end users to build models in a quick and reliable way, using terms from manufacturing. Various tests and a check of model logic are helpful functions when testing validity of the models. Integration with software packages, with business graphics and statistical functions, is convenient in the result presentation phase.
Eirik Borgen
Maritime target identification in flash-ladar imagery
The paper presents new techniques and processing results for automatic segmentation, shape classification, generic pose estimation, and model-based identification of naval vessels in laser radar imagery. The special characteristics of focal plane array laser radar systems such as multiple reflections and intensity-dependent range measurements are incorporated into the algorithms. The proposed 3D model matching technique is probabilistic, based on the range error distribution, correspondence errors, the detection probability of potentially visible model points and false alarm errors. The match algorithm is robust against incomplete and inaccurate models, each model having been generated semi-automatically from a single range image. A classification accuracy of about 96% was attained, using a maritime database with over 8000 flash laser radar images of 146 ships at various ranges and orientations together with a model library of 46 vessels. Applications include military maritime reconnaissance, coastal surveillance, harbor security and anti-piracy operations.
Armbruster, Walter; Hammer, Marcus
Design and performance of single photon APD focal plane arrays for 3-D LADAR imaging
×We describe the design, fabrication, and performance of focal plane arrays (FPAs) for use in 3-D LADAR imaging applications requiring single photon sensitivity. These 32 × 32 FPAs provide high-efficiency single photon sensitivity for three-dimensional LADAR imaging applications at 1064 nm. Our GmAPD arrays are designed using a planarpassivated avalanche photodiode device platform with buried p-n junctions that has demonstrated excellent performance uniformity, operational stability, and long-term reliability. The core of the FPA is a chip stack formed by hybridizing the GmAPD photodiode array to a custom CMOS read-out integrated circuit (ROIC) and attaching a precision-aligned GaP microlens array (MLA) to the back-illuminated detector array. Each ROIC pixel includes an active quenching circuit governing Geiger-mode operation of the corresponding avalanche photodiode pixel as well as a pseudo-random counter to capture per-pixel time-of-flight timestamps in each frame. The FPA has been designed to operate at frame rates as high as 186 kHz for 2 ?s range gates. Effective single photon detection efficiencies as high as 40% (including all optical transmission and MLA losses) are achieved for dark count rates below 20 kHz. For these planar-geometry diffused-junction GmAPDs, isolation trenches are used to reduce crosstalk due to hot carrier luminescence effects during avalanche events, and we present details of the crosstalk performance for different operating conditions. Direct measurement of temporal probability distribution functions due to cumulative timing uncertainties of the GmAPDs and ROIC circuitry has demonstrated a FWHM timing jitter as low as 265 ps (standard deviation is ~100 ps).
Itzler, Mark A.; Entwistle, Mark; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir; Zalud, Peter F.; Senko, Tom; Tower, John; Ferraro, Joseph
Developing a driving simulator based functional object detection task.
The purpose of this study was to validate a driving simulator-based tool for assessing functional visual scanning while driving (Goodenough, 2010) by replicating a previous study and assessing whether the results of the task are moderated by strategic decisions regarding task prioritization. Participants completed a functional object detection task that includes a peripheral target detection task and a central braking response task. Results indicated that the simulator task can identify differences in older and younger participants' abilities to functionally scan the driving environment and these differences appear unaffected by prioritizing either the scanning or braking task. Implications are discussed. PMID:23899199
Goodenough, Richard R; Brooks, Johnell O; Crisler, Matthew C; Rosopa, Patrick J
An object-based methodology for knowledge representation
Energy Technology Data Exchange (ETDEWEB)
An object based methodology for knowledge representation is presented. The constructs and notation to the methodology are described and illustrated with examples. The ``blocks world,`` a classic artificial intelligence problem, is used to illustrate some of the features of the methodology including perspectives and events. Representing knowledge with perspectives can enrich the detail of the knowledge and facilitate potential lines of reasoning. Events allow example uses of the knowledge to be represented along with the contained knowledge. Other features include the extensibility and maintainability of knowledge represented in the methodology.
Kelsey, R.L. [Los Alamos National Lab., NM (United States)|New Mexico State Univ., Las Cruces, NM (United States); Hartley, R.T. [New Mexico State Univ., Las Cruces, NM (United States); Webster, R.B. [Los Alamos National Lab., NM (United States)
Cloud Aggregation and Bursting for Object Based Sharable Environment
Directory of Open Access Journals (Sweden)
Full Text Available Cloud computing promises innate scalability and high availability at low cost. So far cloud storage deployments were subject to big companies but an increasing amount of available open-source systems allow also smaller private cloud installations. In this paper we discuss cloud aggregation and cloud bursting with their empirical review. Based on the review we map class and object in the sharable small clouds for making clouds more efficient. We also consider some of the security concern for the cloud computing for authorized data sharing between clouds.
Mr. Pradeep Kumar Tripathi
Object-based system for stereoscopic videoconferencing with viewpoint adaptation
This paper describes algorithms that were developed for a stereoscopic videoconferencing system with viewpoint adaptation. The system identifies foreground and background regions, and applies disparity estimation to the foreground object, namely the person sitting in front of a stereoscopic camera system with rather large baseline. A hierarchical block matching algorithm is employed for this purpose, which takes into account the position of high-variance feature points and the object/background border positions. Using the disparity estimator's output, it is possible to generate arbitrary intermediate views from the left- and right-view images. We have developed an object-based interpolation algorithm, which produces high-quality results. It takes into account the fact that a person's face has a more or less convex surface. Interpolation weights are derived both from the position of the intermediate view, and from the position of a specific point within the face. The algorithms have been designed for a realtime videoconferencing system with telepresence illusion. Therefore, an important aspect during development was the constraint of hardware feasibility, while sufficient quality of the intermediate view images had still to be retained.
Ohm, Jens-Rainer; Izquierdo, Ebroul
Visual-adaptation-mechanism based underwater object extraction
Due to the major obstacles originating from the strong light absorption and scattering in a dynamic underwater environment, underwater optical information acquisition and processing suffer from effects such as limited range, non-uniform lighting, low contrast, and diminished colors, causing it to become the bottleneck for marine scientific research and projects. After studying and generalizing the underwater biological visual mechanism, we explore its advantages in light adaption which helps animals to precisely sense the underwater scene and recognize their prey or enemies. Then, aiming to transform the significant advantage of the visual adaptation mechanism into underwater computer vision tasks, a novel knowledge-based information weighting fusion model is established for underwater object extraction. With this bionic model, the dynamical adaptability is given to the underwater object extraction task, making them more robust to the variability of the optical properties in different environments. The capability of the proposed method to adapt to the underwater optical environments is shown, and its outperformance for the object extraction is demonstrated by comparison experiments.
Chen, Zhe; Wang, Huibin; Xu, Lizhong; Shen, Jie
State-based modeling and object extraction from echocardiogram video.
In this paper, we propose a hierarchical state-based model for representing an echocardiogram video. It captures the semantics of video segments from dynamic characteristics of objects present in each segment. Our objective is to provide an effective method for segmenting an echo video into view, state, and substate levels. This is motivated by the need for building efficient indexing tools to support better content management. The modeling is done using four different views, namely, short axis, long axis, apical four chamber, and apical two chamber. For view classification, an artificial neural network is trained with the histogram of a region of interest of each video frame. Object states are detected with the help of synthetic M-mode images. In contrast to traditional single M-mode, we present a novel approach named sweep M-mode for state detection. We also introduce radial M-mode for substate identification from color flow Doppler 2-D imaging. The video model described here represents the semantics of video segments using first-order predicates. Suitable operators have been defined for querying the segments. We have carried out experiments on 20 echo videos and compared the results with manual annotation done by two experts. View classification accuracy is 97.19%. Misclassification error of the state detection stage is less than 13%, which is within acceptable range since only frames at the state boundaries are found to be misclassified. PMID:18693504
Roy, Aditi; Sural, Shamik; Mukherjee, Jayanta; Majumdar, Arun K
Novel technique: a pupillometer-based objective chromatic perimetry
Evaluation of visual field (VF) is important for clinical diagnosis and patient monitoring. The current VF methods are subjective and require patient cooperation. Here we developed a novel objective perimetry technique based on the pupil response (PR) to multifocal chromatic stimuli in normal subjects and in patients with glaucoma and retinitis pigmentosa (RP). A computerized infrared video pupillometer was used to record PR to short- and long-wavelength stimuli (peak 485 nm and 620 nm, respectively) at light intensities of 15-100 cd-s/m2 at thirteen different points of the VF. The RP study included 30 eyes of 16 patients and 20 eyes of 12 healthy participants. The glaucoma study included 22 eyes of 11 patients and 38 eyes of 19 healthy participants. Significantly reduced PR was observed in RP patients in response to short-wavelength stimuli at 40 cd-s/m2 in nearly all perimetric locations (P group showed significantly reduced PR to long- and short-wavelength stimuli at high intensity in all perimetric locations (P <0.05). The PR of glaucoma patients was significantly lower than normal in response to short-wavelength stimuli at low intensity mostly in central and 20° locations (p<0.05). This study demonstrates the feasibility of using pupillometer-based chromatic perimetry for objectively assessing VF defects and retinal function and optic nerve damage in patients with retinal dystrophies and glaucoma. Furthermore, this method may be used to distinguish between the damaged cells underlying the VF defect.
Rotenstreich, Ygal; Skaat, Alon; Sher, Ifat; Kolker, Andru; Rosenfeld, Elkana; Melamed, Shlomo; Belkin, Michael
Robust B+ -Tree-Based Indexing of Moving Objects
DEFF Research Database (Denmark)
With the emergence of an infrastructure that enables the geo-positioning of on-line, mobile users, the management of so-called moving objects has emerged as an active area of research. Among the indexing techniques for efficiently answering predictive queries on moving-object positions, the recent Bx-tree is based on the B+-tree and is relatively easy to integrate into an existing DBMS. However, the Bx-tree is sensitive to data skew. This paper proposes a new query processing algorithm for the Bx-tree that fully exploits the available data statistics to reduce the query enlargement that is needed to guarantee perfect recall, thus significantly improving robustness. The new technique is empirically evaluated and compared with four other approaches and with the TPR-tree, a competitor that is based on the R*-tree. The results indicate that the new index is indeed more robust than its predecessor?it significantly reduces the number of I/O operations per query for the workloads considered. In many settings, the TPR-tree is outperformed as well.
Jensen, Christian SŘndergaard; Tiesyte, Dalia
Defining competency-based evaluation objectives in family medicine
Abstract Objective To develop and describe observable evaluation objectives for assessing competence in professionalism, which are grounded in the experience of practising physicians. Design Modified nominal group technique. Setting The College of Family Physicians of Canada in Mississauga, Ont. Participants An expert group of 7 family physicians and 1 educational consultant, all of whom had experience in assessing competence in family medicine. Group members represented the Canadian context with respect to region, sex, language, community type, and experience. Methods Using an iterative process, the expert group defined a list of observable behaviours that are indicative of professionalism, or not, in the family medicine setting. Themes relate to professional behaviour in family medicine; specific observable behaviours are those that family physicians believe are indicative of professionalism for each theme. Main findings The expert group identified 12 themes and 140 specific observable behaviours to assist in the observation and discussion of professional behaviour in family medicine workplace settings. Conclusion Competency-based education literature emphasizes the importance of formative evaluation and feedback. Such feedback is particularly challenging in the domain of professionalism because of its personal nature and the potential for emotional reactions. Effective dialogue between learners and teachers begins with clear expectations and reference to descriptions of relevant, specific behaviour. This research has generated a competency-based resource to assist the assessment of professional behaviour in family medicine educational programs. PMID:23064939
Donoff, Michel; Lawrence, Kathrine; Allen, Tim; Brailovsky, Carlos; Crichton, Tom; Bethune, Cheri; Laughlin, Tom; Wetmore, Stephen
Power System Information Delivering System Based on Distributed Object
In recent years, improvement in computer performance and development of computer network technology or the distributed information processing technology has a remarkable thing. Moreover, the deregulation is starting and will be spreading in the electric power industry in Japan. Consequently, power suppliers are required to supply low cost power with high quality services to customers. Corresponding to these movements the authors have been proposed SCOPE (System Configuration Of PowEr control system) architecture for distributed EMS/SCADA (Energy Management Systems / Supervisory Control and Data Acquisition) system based on distributed object technology, which offers the flexibility and expandability adapting those movements. In this paper, the authors introduce a prototype of the power system information delivering system, which was developed based on SCOPE architecture. This paper describes the architecture and the evaluation results of this prototype system. The power system information delivering system supplies useful power systems information such as electric power failures to the customers using Internet and distributed object technology. This system is new type of SCADA system which monitors failure of power transmission system and power distribution system with geographic information integrated way.
Tanaka, Tatsuji; Tsuchiya, Takehiko; Tamura, Setsuo; Seki, Tomomichi; Kubota, Kenji
Object Persistence: A Framework Based On Design Patterns
Digital Repository Infrastructure Vision for European Research (DRIVER)
The poster presents a framework for providing object persistence in object-oriented programming languages without modifying the run-time system or the language itself. The framework does not rely on any kind of special programming language features. It only uses basic object-oriented programming techniques, and is therefore implementable in any object-oriented programming language.
Kienzle, Jo?rg; Romanovsky, Alexander
Mobile object retrieval in server-based image databases
The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.
Manger, D.; Pagel, F.; Widak, H.
Objective skill evaluation for laparoscopic training based on motion analysis.
Performing laparoscopic surgery requires several skills, which have never been required for conventional open surgery. Surgeons experience difficulties in learning and mastering these techniques. Various training methods and metrics have been developed to assess and improve surgeon's operative abilities. While these training metrics are currently widely being used, skill evaluation methods are still far from being objective in the regular laparoscopic skill education. This study proposes a methodology of defining a processing model that objectively evaluates surgical movement performance in the routine laparoscopic training course. Our approach is based on the analysis of kinematic data describing the movements of surgeon's upper limbs. An ultraminiaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The data processing model was trained by using the subjects' motion features acquired from the WB-3 system and further validated to classify the expertise levels of the subjects with different laparoscopic experience. Experimental results show that the proposed methodology can be efficiently used both for quantitative assessment of surgical movement performance, and for the discrimination between expert surgeons and novices. PMID:23204271
Lin, Zhuohua; Uemura, Munenori; Zecca, Massimiliano; Sessa, Salvatore; Ishii, Hiroyuki; Tomikawa, Morimasa; Hashizume, Makoto; Takanishi, Atsuo
Multi-Object Optimization Based RV Selection Algorithm for VCN
Directory of Open Access Journals (Sweden)
Full Text Available Vehicular communication network (VCN has recently received considerable attention both from academia and industry. In VCN, vehicles are expected to be capable of communicating with other vehicles as well as stationary infrastructures, i.e., the access points (APs of wireless access networks. In the case that the direct connection between a source vehicle (SV and APs is inaccessible, relay vehicles (RVs can be applied for supporting multi-hop connection between SVs and APs. In this paper, a multi-object based RV selection algorithm for VCN is proposed, which jointly considers the characteristics of physical channel, link status between SVs and RVs, the bandwidth and delay characteristics of RVs and user service requirements. The utility functions of both SVs and RVs are modeled and a multi-object optimization problem is formulated. Applying ideal point method, the problem can be solved and the optimal SV-RV pairs can be obtained. Simulation results demonstrate that compared to previous algorithms, the proposed algorithm offers better performance in terms of user throughput, successful transmission rate and average transmission delay
Rong Chai
A full featured component object oriented based architecture testing tool
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Full Text Available Object-orientation has rapidly become accepted as the preferred paradigm for large-scale system design. The product created during Software Development effort has to be tested since bugs may get introduced during its development. In this research work we 1 establish a requirement specification for a comprehensive software testing tool. 2 This will involve studying the feature set offered by existing software testing tools and their limitations. This will be able to overcome the limitations of limited feature sets of existing software tools. 3 To propose a comprehensive architecture of a software testing tool, this will include most of the features required for a software testing tool. 4 The purpose is to avoid compatibility problems which are incurred by interfacing various tools to utilize individual tools strengths. Also, as different tools are having different user interfaces, it takes effort to learn, how to use them. A full featured, comprehensive tool is a solution to all of these problems. We intend to propose the object oriented methodology based architectures for the comprehensive tool.
Sarita Singh Bhadauria
Implementation and Comparison of Kernel and Silhouette Based Object Tracking
Directory of Open Access Journals (Sweden)
Full Text Available Object tracking in video sequences is one of the important ongoing exploration areas in the field of computer vision. Computer vision is an arena that comprises methods for acquiring, processing, analyzing images and also covers the essential technology of automatic image analysis which is used in various fields. The aim of object tracking is to find the trajectory of the target objects through a number of frames from an image sequence. Object Tracking is identification of interesting object, especially on tracking of walkers or moving vehicles. Tracking is an interesting problem owing to, object occlusion, varying of illumination, unexpected object motion and camera motion. Normally many algorithms were developed for successful tracking. Object Tracking is mainly classified of three stages: object extraction, object recognition and tracking, and decisions about activities. In this paper we have implemented some algorithms and comparison table are analyzed.
Mr. Joshan Athanesious J , Mr. Suresh P
Image Object Detection Algorithm Based on Improved Gaussian Mixture Model
Directory of Open Access Journals (Sweden)
Full Text Available Aiming at poor adaptability to illumination variation and single learning rate in traditional Gaussian mixture model, an improved moving object detection algorithm based on adaptive Gaussian mixture model is proposed in this paper, so as to achieve the goal of a self-adaptive background updating model. In this paper, we analyze the existed algorithms and put forward the method to make use of color histogram matching algorithm, through introduction of illumination variation factor and update-counter of model parameter and the components number with self-adaptive selection employed to adaptively adjust learning rate, in order to greatly reduce the computation time of the algorithm and improve the real-time performance. The experiment results show that the new method can effectively adapt the scene, and has more good expansibility, robustness and stability than traditional Gaussian mixture model.
Xing-liang Li
Underwater object detection technology based on polarization image fusion
The performance of the traditional underwater optical imaging systems is ultimately limited by the absorption and scattering properties of the water substance. Polarimetric imaging can be used to remove degradation effects, and can be applied to high-level vision tasks, such as object classification and recognition and camouflage identification, etc. The method of improving contrast was presented by polarization imaging. The polarization images when the angle of polarization are 0°, 45°, 90° and 135°, the gray levels of the images are calculated by program. There are much complementary and redundancy information among the polarization images. According to the character of parameter I of stokes vector, degree of linear polarization (DoLP) and the angle of polarization (AoP), A RGB false color based polarimetric images fusion are given to enhance the contrast.
Li, Yongguo; Wang, Shiming
RFID and IP Based Object Identification in Ubiquitous Networking
Digital Repository Infrastructure Vision for European Research (DRIVER)
Ubiquitous networking is an integrated part of future networking technology that can provide capabilities for connecting all of objects (computers, human, PDAs, cell phones etc.) in future network. It has to meet the challenge of seamless connection for communication between human and objects in internet infrastructure. Unique object identification is very much important to make the communication between objects possible. RFID tag can be used as unique identifier to identify a physical object...
Nisha Vaghela; Parikshit Mahalle
Performance Evaluation of Raster Based Shape Vectors in Object Recognition
Directory of Open Access Journals (Sweden)
Full Text Available Object recognition is still an impediment in the field of computer vision and multimedia retrieval. Defining an object model is a critical task. Shape information of an object play a critical role in the process of object recognition. Extraction of boundary information of an object from the multimedia data and classifying this information with associated objects is the primary step towards object recognition. Rasters play an important role while computing object boundary. The trade-off lies with the dimensionality of the object versus computational cost while achieving maximum efficiency. In this treatise an attempt is made to evaluate the performance of circular and spiral raster models in terms of average retrieval efficiency and computational cost.
Akbar khan
Model-Based Object Tracking in Cluttered Scenes with Occlusions
Digital Repository Infrastructure Vision for European Research (DRIVER)
We propose an efficient method for tracking 3D modelled objects in cluttered scenes. Rather than tracking objects in the image, our approach relies on the object recognition aspect of tracking. Candidate matches between image and model features define volumes in the space of transformations. The volumes of the pose space satisfying the maximum number of correspondences are those that best align the model with the image. Object motion defines a trajectory in the pose space. We give some result...
Jurie, Fre?de?ric
Momentum Based Level Set Method For Accurate Object Tracking
Directory of Open Access Journals (Sweden)
Full Text Available This paper proposes a novel object tracking method that is robust to a cluttered background and large motion. First, a posterior probability measure (PPM is adopted to locate the object region. Then the momentumbased level set is used to evolve the object contour in order to improve the tracking precision. To achieve rough object localization, the initial target position is predicted and evaluated by the Kalman filter and the PPM, respectively. In the contour evolution stage, the active contour is evolved on the basis of an object feature image. This method can acquire more accurate target template as well as target center. The comparison between our method and the kernelbased method demonstrates that our method can effectively cope with the deformation of object contour and the influence of the complex background when similar colors exist nearby. Experimental results show that our method has higher tracking precision.
Haocheng Le
Object Recognition Algorithm Utilizing Graph Cuts Based Image Segmentation
Directory of Open Access Journals (Sweden)
Full Text Available This paper concentrates on designing an object recognition algorithm utilizing image segmentation. The main innovations of this paper lie in that we convert the image segmentation problem into graph cut problem, and then the graph cut results can be obtained by calculating the probability of intensity for a given pixel which is belonged to the object and the background intensity. After the graph cut process, the pixels in a same component are similar, and the pixels in different components are dissimilar. To detect the objects in the test image, the visual similarity between the segments of the testing images and the object types deduced from the training images is estimated. Finally, a series of experiments are conducted to make performance evaluation. Experimental results illustrate that compared with existing methods, the proposed scheme can effectively detect the salient objects. Particularly, we testify that, in our scheme, the precision of object recognition is proportional to image segmentation accuracy
Zhaofeng Li
Partial Evaluation for Class-Based Object-Oriented Languages
DEFF Research Database (Denmark)
Object-oriented programming facilitates the development of generic software, but at a significant cost in terms of performance. We apply partial evaluation to object-oriented programs, to automatically map generic software into specific implementations. In this paper we give a concise, formal description of a simple partial evaluator for a minimal object-oriented language, and give directions for extending this partial evaluator to handle realistic programs.
Schultz, Ulrik Pagh
Feature based recognition of submerged objects in holographic imagery
The ability to autonomously sense and characterize underwater objects in situ is desirable in applications of unmanned underwater vehicles (UUVs). In this work, underwater object recognition was explored using a digital holographic system. Two experiments were performed in which several objects of varying size, shape, and material were submerged in a 43,000 gallon test tank. Holograms were collected from each object at multiple distances and orientations, with the imager located either outside the tank (looking through a porthole) or submerged (looking downward). The resultant imagery from these holograms was preprocessed to improve dynamic range, mitigate speckle, and segment out the image of the object. A collection of feature descriptors were then extracted from the imagery to characterize various object properties (e.g., shape, reflectivity, texture). The features extracted from images of multiple objects, collected at different imaging geometries, were then used to train statistical models for object recognition tasks. The resulting classification models were used to perform object classification as well as estimation of various parameters of the imaging geometry. This information can then be used to inform the design of autonomous sensing algorithms for UUVs employing holographic imagers.
Ratto, Christopher R.; Beagley, Nathaniel; Baldwin, Kevin C.; Shipley, Kara R.; Sternberger, Wayne I.
Multi-object spectral imaging based on MEMS
The primary compact high resolution imaging spectrometer was developed and reported. Due to its numerous wave bands the original image data is always in a huge scale and costs a tremendous process overhead, but the data amount of the region of interest is as a rule in the order of thousandth, if not less, of that of the whole push-broom region. With a digital micromirror device (DMD), only the region of interesting object is imaged by the imaging spectrometer, which results in a distinct reduction of data quantity and a high data compression ratio. A DMD of high turning rate and residential time adjustability is used as a spatial light modulator to fulfill the object selection function. It is placed after the fore objective and able to reflect the object to either the panchromatic CCD camera channel or the imaging spectrometer channel. The position of the object can be firstly determined through the image interpretation from panchromatic imaging channel and a DMD control command is executed to switch the corresponding mirrors to the imaging spectrometer channel, thus only the object region of interest is imaged by the spectrometer. The multiple objects of both printed patterns and real leaves are accurately determined and selected according to their different locality and shape features. The panchromatic and hyperspectral image data are both collected for further effective object recognition.
Chen, Yuheng; Ji, Yiqun; Zhou, Jiankang; Chen, Xinhua; Shen, Weimin
Directory of Open Access Journals (Sweden)
Full Text Available OBJETIVO: Avaliar a eficácia e a segurança do LASIK (ceratotomia lamelar pediculada hipermetrópico utilizando-se o sistema Ladar Vision de excimer laser. MÉTODOS: Foram analisados, retrospectivamente, 28 olhos de 17 pacientes com hipermetropia de +1,00 a +3,00 D (grupo 1 e 29 olhos de 18 pacientes com hipermetropia de + 3,25 a + 6,00 D (grupo 2, submetidos ŕ cirurgia de LASIK, com o Sistema Ladar Vision de excimer laser. Acuidade visual sem correçăo, melhor acuidade visual corrigida e refraçăo sob cicloplegia foram avaliadas em um, tręs e seis meses de pós-operatório. RESULTADOS: No grupo 1, o equivalente esférico médio pré-operatório, sob cicloplegia, era de + 2,14 ± 0,64 D, passando para + 0,44 ± 0,38 D no sexto męs de pós-operatório. No grupo 2, o equivalente esférico médio pré-operatório era de +4,26 ± 0,75 D, diminuindo para +1,14 ± 0,63 D no sexto męs de pós-operatório. 3,4% dos olhos do grupo 2 perderam tręs linhas de visăo no primeiro męs de pós-operatório. No grupo 1, năo houve perda de duas ou mais linhas de visăo. CONCLUSŐES: O LASIK hipermetrópico com o sistema Ladar Vision mostrou-se procedimento eficaz e seguro. Pacientes do grupo 2 parecem estar sob maior risco de perda de linhas de melhor acuidade visual corrigida no pós-operatório.PURPOSE: To analyze the efficacy and safety of hyperopic laser in situ keratomileusis using the Ladar Vision excimer laser system. METHODS: Twenty-eight eyes of 17 patients with hyperopia from +1.00 to +3.00 D (group 1, and 29 eyes of 18 patients with hyperopia from +3.25 to +6.00 D (group 2 that had LASIK for hyperopia with the Ladar Vision, were retrospectively analyzed. Uncorrected visual acuity, best spectacle-corrected visual acuity and cycloplegic refraction were evaluated 1 , 3 and 6 months after surgery. RESULTS: In group 1, the mean preoperative cycloplegic spherical equivalent (SE was +2.14 ± 0.64 D and 6-month postoperative SE was +0.44 ± 0.38 D. In group 2, the mean preoperative SE was +4.26 ± 0.75 D and the 6-month postoperative SE was +1.14 ± 0.63 D. 3.4% of the eyes in group 2 and none of the eyes in group 1 lost 2 or more lines of best spectacle-corrected visual acuity in the first postoperative month. CONCLUSIONS: LASIK with the Ladar Vision excimer laser system is an effective and safe procedure to correct hyperopia. Patients in group 2 appear to be at greater risk for loss of lines of best spectacle-corrected visual acuity.
Larissa Madeira Nunes
Scientific Electronic Library Online (English)
Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese OBJETIVO: Avaliar a eficácia e a segurança do LASIK (ceratotomia lamelar pediculada) hipermetrópico utilizando-se o sistema Ladar Vision de excimer laser. MÉTODOS: Foram analisados, retrospectivamente, 28 olhos de 17 pacientes com hipermetropia de +1,00 a +3,00 D (grupo 1) e 29 olhos de 18 pacientes [...] com hipermetropia de + 3,25 a + 6,00 D (grupo 2), submetidos ŕ cirurgia de LASIK, com o Sistema Ladar Vision de excimer laser. Acuidade visual sem correçăo, melhor acuidade visual corrigida e refraçăo sob cicloplegia foram avaliadas em um, tręs e seis meses de pós-operatório. RESULTADOS: No grupo 1, o equivalente esférico médio pré-operatório, sob cicloplegia, era de + 2,14 ± 0,64 D, passando para + 0,44 ± 0,38 D no sexto męs de pós-operatório. No grupo 2, o equivalente esférico médio pré-operatório era de +4,26 ± 0,75 D, diminuindo para +1,14 ± 0,63 D no sexto męs de pós-operatório. 3,4% dos olhos do grupo 2 perderam tręs linhas de visăo no primeiro męs de pós-operatório. No grupo 1, năo houve perda de duas ou mais linhas de visăo. CONCLUSŐES: O LASIK hipermetrópico com o sistema Ladar Vision mostrou-se procedimento eficaz e seguro. Pacientes do grupo 2 parecem estar sob maior risco de perda de linhas de melhor acuidade visual corrigida no pós-operatório. Abstract in english PURPOSE: To analyze the efficacy and safety of hyperopic laser in situ keratomileusis using the Ladar Vision excimer laser system. METHODS: Twenty-eight eyes of 17 patients with hyperopia from +1.00 to +3.00 D (group 1), and 29 eyes of 18 patients with hyperopia from +3.25 to +6.00 D (group 2) that [...] had LASIK for hyperopia with the Ladar Vision, were retrospectively analyzed. Uncorrected visual acuity, best spectacle-corrected visual acuity and cycloplegic refraction were evaluated 1 , 3 and 6 months after surgery. RESULTS: In group 1, the mean preoperative cycloplegic spherical equivalent (SE) was +2.14 ± 0.64 D and 6-month postoperative SE was +0.44 ± 0.38 D. In group 2, the mean preoperative SE was +4.26 ± 0.75 D and the 6-month postoperative SE was +1.14 ± 0.63 D. 3.4% of the eyes in group 2 and none of the eyes in group 1 lost 2 or more lines of best spectacle-corrected visual acuity in the first postoperative month. CONCLUSIONS: LASIK with the Ladar Vision excimer laser system is an effective and safe procedure to correct hyperopia. Patients in group 2 appear to be at greater risk for loss of lines of best spectacle-corrected visual acuity.
Larissa Madeira, Nunes; Cláudia Maria, Francesconi; Mauro, Campos; Paulo, Schor.
Prediction Based Object Recovery Using Sequential Monte Carlo Method
Directory of Open Access Journals (Sweden)
Full Text Available Object tracking in wireless sensor networks has been a hot research topic in a recent scenario, due to its wide-ranging applications. Most object tracking uses prediction scheme to minimize the energy consumption and to maintain low missing rate in a sensor network. However objects need to be localize, when object was found missing during tracking process. In this paper, we proposed sequential Monte Carlo method (SMCM to accurately estimate the location of the missing object and the extensive simulations are also shown to demonstrate the effectiveness of the proposed sequential Monte Carlomethod against the centroid and multilatertion methods to evaluate its performance in terms of network energy consumption and localization error.
Pavalarajan Sangaiah
Target recognition for small samples of ladar range image using classifier ensembles
The range image has received considerable attention in the automatic target recognition field; however, a mass of range images are generally difficult to be collected in a real application. Therefore, with small samples of laser radar (ladar) range images, classifier ensembles-support vector machine (SVM) ensembles and back propagation neural networks (BPNN) ensembles-are applied to improve the performance of target recognition in this paper. There are three aspects of experiments. First, the performances of SVM and BPNN are compared with different numbers of training sets. Secondly, the SVM ensembles and the BPNN ensembles are applied to improve the performance. Thirdly, the performances of the SVM ensembles and the BPNN ensembles are analyzed with the view angle of the tested samples changing while the view angle of the trained samples is invariant. The experimental results demonstrate that the recognition rate is effectively improved by classifier ensembles. The SVM is superior to the BPNN when the number of the training sets is not less than the feature dimensions; however, the BPNN has a better approximating ability when the number of training sets is small and lower than feature dimensions.
Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi
Digital Repository Infrastructure Vision for European Research (DRIVER)
For security and military applications, long-range automatic target recognition is a very important task. Therefore, in addition to a 2-D passive or active intensity image, 3-D information of a target is desirable. Besides a LADAR system, also a Gated-Viewing (GV) system can provide depth information by simply sliding the gate through the scenery. In this paper, the GV camera LIVARŇ 500 (Intevac, 640 × 480 pixels (binning mode), EBCMOS) is compared to a 3-D Flash LADAR camera (Advanced Scie...
Go?hler, Benjamin; Lutzmann, Peter
A biological hierarchical model based underwater moving object detection.
Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results. PMID:25140194
Shen, Jie; Fan, Tanghuai; Tang, Min; Zhang, Qian; Sun, Zhen; Huang, Fengchen
Moving Object Tracking Based on EKF and Mean Shift
Directory of Open Access Journals (Sweden)
Full Text Available Kalman filter is a traditional method of optimal estimation which is appropriate for linear and Gaussian model. But in practical application, there are many nonlinear and non-Gaussian models, and Extend Kalman filter is mainly used for nonlinear model. In this paper, Extend Kalman filter and Mean shift are combined to track the moving object. Firstly Extend Kalman filter is used to predict the next possible position of the object at target center. Secondly, mean shift is adapted to search moving target later. Experiment results show that this method reduces the time for searching object, thus it improves the speed of tracking target.
Liwei Chen
A Moving Object Detection Algorithm Based on Color Information
International Nuclear Information System (INIS)
This paper designed a new algorithm of moving object detection for the aim of quick moving object detection and orientation, which used a pixel and its neighbors as an image vector to represent that pixel and modeled different chrominance component pixel as a mixture of Gaussians, and set up different mixture model of Gauss for different YUV chrominance components. In order to make full use of the spatial information, color segmentation and background model were combined. Simulation results show that the algorithm can detect intact moving objects even when the foreground has low contrast with background
Object Tracking Approach based on Mean Shift Algorithm
Directory of Open Access Journals (Sweden)
Full Text Available Object tracking has always been a hotspot in the field of computer vision, which has a range of applications in real world. The object tracking is a critical task in many vision applications. The main steps in video analysis are: detection of interesting moving objects and tracking of such objects from frame to frame. Most of tracking algorithms use pre-defined methods to process. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences. Experimental results show that the Mean shift tracker is effective and robust tracking method.
Xiaojing Zhang
Gesture-Based Control of Spaces and Objects in Augmented.
A multi-modal system integrating computer vision and speech recognition to enable interaction with virtual spaces/objects by natural gestures and speech is described. Computer vision algorithms are employed to measure and interpret hand/finger movement of...
Y. Yacoob, L. Davis
Industrial Micro Electro Mechanical Systems (MEMS) developers are rapidly bringing to demonstration inertial radio frequency, and optical MEMS devices and components. The Army has a requirement for compact, highly reliable, and inexpensive laser beam steering components for missile seekers and unmanned aerial vehicles remote sensing components to provide a fast scanning capability for pointing, acquisition, tracking, and data communication. The coupling of this requirement with recent developments in the micro-optics area, has led scientists and engineers at the Army Aviation and Missile Command (AMCOM) to consider optical MEMS-based phased arrays, which have potential applications in the commercial industry as well as in the military, as a replacement for gimbals. Laser beam steering in commercial applications such as free space communicataion, scanning display, bar-code reading, and gimbaled seekers; require relatively large monolithic micro-mirrors to accomplish the required optical resolution. The Army will benefit from phased arrays composed of relatively small micro-mirrors that can be actuated through large deflection angles with substantially reduced volume times. The AMCOM Aviation and Missile Research, Development, and Engineering Center (AMRDEC) has initiated a research project to develop MEMS-based phased arrays for use in a small volume, inexpensive Laser Detection and Ranging (LADAR) seeker that is particularly attractive because of its ability to provide large field-of-regard and autonomous target acquisition for reconnaissance mission applications. The primary objective of the collaborative project with the Defence Advanced Research Projects Agency (DARPA) is to develop a rugged, MEMS-based phased arrays for incorporation into the 2-D scanner of a LADAR seeker. Design challenges and approach to achieving performance requirements will be discussed.
Ruffin, Paul B.
Drifting Recovery Base Concept for GEO Derelict Object Capture
Over 250 objects hover within 6 m/sec of perfect geostationary orbit. Over half of these objects lie within 0.1 m/sec of the GEO velocity. Such items have 62% of the total velocity required to achieve Earth gravitational escape. A conceptual architecture is proposed to clean this orbit area of derelict objects while providing a demonstration mission for many facets of future asteroid mining operations. These near-GEO objects average nearly 2000kg each, consisting of (typically functioning) power systems, batteries, and large quantities of components and raw aerospace-grade refined materials. Such a demonstration collection system could capture, collect and remove all GEO derelict objects in an international effort to create a depot of components and of aerospace-grade raw materials--with a total mass greater than that of the International Space Station--as a space scrap depot ready for transfer to lunar or Mars orbit, using only two heavy-lift launches and 2-3 years of on-orbit operations.
Bacon, John B.
MOPSO-based multi-objective TSO planning considering uncertainties
DEFF Research Database (Denmark)
The concerns of sustainability and climate change have posed a significant growth of renewable energy associated with smart grid technologies. Various uncertainties are the major problems need to be handled by transmission system operator (TSO) planning. This paper mainly focuses on three uncertain factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment cost and power outage cost. A two-phase MOPSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity ofPareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved MOPSO have been verified by the 77-bus system.
Wang, Qi; Zhang, Chunyu
Evaluation of Object Based Video Retrieval Using SIFT
Directory of Open Access Journals (Sweden)
Full Text Available In Video Retrieval system, each video that is stored in the database has its features extracted and compared to the features of the query image. The local invariant features are obtained for all frames in a sequence and tracked throughout the shot to extract stable features. Proposed work is to retrieve video from the database by giving query as an object. Video is firstly converted into frames, these frames are then segmented and an object is separated from the image. Then features are extracted from object image by using SIFT features. Features of the video database obtained by the segmentation and feature extraction using SIFT feature are matched by Nearest Neighbor Search (NNS. In this paper we have evaluated the proposed video retrieval system. The proposed method is better than previous video retrieval methods because it is invariant to illumination changes.
Shradha Gupta
Aircraft Simulator Designing based on Object Oriented Methodologies
Directory of Open Access Journals (Sweden)
Full Text Available Object plays a very important role in comparing softwareentities. But the role of object when it comes to simulationfield is still undefined. Several standards have been createdwith the purpose to provide a design solution for the projectof Simulators. The design solution reported in this paperaggregates the principles of both software and simulatorarchitectures. The objective is to invert the top-downstrategy of model-driven development with the SimulationModel Portability (SMP standard into a bottom-updevelopment process of a SMP framework. The paper alsoprovides solution for two development lines of two differentframeworks: the first is the SMP framework that changesaccording to the design models, and the second is aframework designed to support the development ofreusable behavior implementations
Rahul Kosarwal
Object Tracking Based on Camshift with Multi-feature Fusion
Directory of Open Access Journals (Sweden)
Full Text Available It is very hard for traditional Camshift to survive of drastic interferences and occlusions of similar objects. This paper puts forward an innovative tracking method using Camshift with multi-feature fusion. Firstly, SIFT features and edge features of the Camshift in RGB space are counted to reduce the probability of disruption by occlusion and clutter. Then, the texture features are collected to resolve the problems of analogue interference, the texture similarity between current frame and previous frames are calculated to determine the object area. The paper also describes the GM(1,1 prediction model, which could solve the occlusion problems in a novel way. Finally, through the motion trajectory, it can anticipate the exact position of the object. The results of several tracking tasks prove that our method has solved problems of occlusions, interferences and shadows. And it performs well in both tracking robustness and computational efficiency.
Zhiyu Zhou
Dynamic Cell Formation based on Multi-objective Optimization Model
Directory of Open Access Journals (Sweden)
Full Text Available In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.
Guozhu Jia
JBOOM: Java Based Object Oriented Model of Software Configuration Management
Directory of Open Access Journals (Sweden)
Full Text Available Most of the present Software Configuration Management systems deal with version and configurations in the form of files and directories, the need today is to have a Software Configuration Management system that handles versions and configurations directly in terms of functions (program module. A major objective of this research is the use of Java in the Software Configuration Management systems. An object-oriented language provides both design and implementation in an integrated manner. We have proposed a model that expresses change evolution in terms of class hierarchies. As the changes evolve so does the class hierarchy, it can be further extended and existing classes can be extended.
Bhavya Mehta
Ontology-Based Annotation of Learning Object Content
The paper proposes a framework for building ontology-aware learning object (LO) content. Previously ontologies were exclusively employed for enriching LOs' metadata. Although such an approach is useful, as it improves retrieval of relevant LOs from LO repositories, it does not enable one to reuse components of a LO, nor to incorporate an explicit…
Gasevic, Dragan; Jovanovic, Jelena; Devedzic, Vladan
Objective, Way and Method of Faculty Management Based on Ergonomics
The core problem that influences educational quality of talents in colleges and universities is the faculty management. Without advanced faculty, it is difficult to cultivate excellent talents. With regard to some problems in present faculty construction of colleges and universities, this paper puts forward the new objectives, ways and methods of…
WANG, Hong-bin; Liu, Yu-hua
Object-Based Epistemology at a Creationist Museum
In a regional young-earth creationist museum, objects are presented as if they speak for themselves, purportedly embodying proof that the earth is less than 10,000 years old, that humans have lived on earth throughout its history, and that dinosaurs and humans lived simultaneously. In public lectures, tours, and displays, museum associates…
Wendel, Paul J.
Manipulating deformable linear objects - Vision-based recognition of contact state transitions -
Digital Repository Infrastructure Vision for European Research (DRIVER)
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation of the object with respect to an obstacle and are derived from the object image and its features. Therefore, the object is segmented from a standard video frame using a fast segmentation algorithm. Several object features are presen...
Abegg, Frank; Henrich, Dominik; Wo?rn, Heinz
Adaptability Evaluation of Enterprise Information Systems Based on Object-based Knowledge Mesh
Directory of Open Access Journals (Sweden)
Full Text Available To apply enterprise information systems more widely, it is necessary to evaluate their adaptability. In this paper, firstly, an index set of adaptability evaluation system based on object-based knowledge mesh (OKM is proposed and then, according to goal-question-metrics (GQM for enterprise information systems, and the quantitative measurement is given. Second, based on similarity to ideal solution (TOPSIS, the evaluation model is built and the adaptability evaluation (AE algorithm is proposed to evaluate enterprise information systems' adaptability. Finally, the application of the evaluation system and AE algorithm is verified through an example, which provides quantitative references for evaluating and optimizing enterprise information systems' adaptability.
Chaogai Xue
Application of Object-Based Industrial Controls for Cryogenics
The first application of the CERN Unified Industrial Control system (UNICOS) has been developed for the 1.8 K refrigerator at point 1.8 in mid-2001. This paper presents the engineering methods used for application development, in order to reach the objectives of maintainability and reusability, in the context of a development done by an external consortium of engineering firms. It will also review the lessons learned during this first development and the improvements planned for the next applications.
Casas-Cubillos, J; Gomes, P; Pezzetti, M; Sicard, Claude Henri; Varas, F J
Mobile object retrieval in server-based image databases
Digital Repository Infrastructure Vision for European Research (DRIVER)
The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are re...
Manger, Daniel; Pagel, Frank; Widak, Heiko
Finger Readjustment Algorithm for Object Manipulation Based on Tactile Information
Digital Repository Infrastructure Vision for European Research (DRIVER)
This paper presents a novel algorithm which registers pressure information from tactile sensors installed over the fingers of a robotic hand in order to perform manipulation tasks with objects. This algorithm receives as an input the joint trajectories of the fingers which have to be executed and adapts it to the real contact pressure of each finger in order to guarantee that undesired slippage or contact?breaking is avoided during the execution of the manipulation task. This algorithm has ...
Juan Antonio Corrales Ramo?n; Fernando Torres Medina; Ve?ronique Perdereau
Object simplification using a skeleton-based weight function
Digital Repository Infrastructure Vision for European Research (DRIVER)
In this paper, we present a novel method for template simplification, where the template is used to find interesting objects within an image. In this way, we improve computational performance since less template points are matched using a simplified template. Moreover, we increase the reliability of the matching as we keep template points with focusing on the main shape behavior (skeleton) of the template. The theoretical background of the simplification is derived through the centroidal V...
Hajdu, A.; Giamas, C.; Vretos, N.; Pitas, I.
A Knowledge-Based Approach to Describe and Adapt Learning Objects
Our claim is that semantic metadata are required to allow a real reusing and assembling of learning objects. Our system is based on three models used to describe the domain, learners, and learning objects. The learning object model is inspired from knowledge representation proposals. A learning object can be reused directly or can be combined with…
Bouzeghoub, Amel; Defude, Bruno; Duitama, John Freddy; Lecocq, Claire
Finger Readjustment Algorithm for Object Manipulation Based on Tactile Information
Directory of Open Access Journals (Sweden)
Full Text Available This paper presents a novel algorithm which registers pressure information from tactile sensors installed over the fingers of a robotic hand in order to perform manipulation tasks with objects. This algorithm receives as an input the joint trajectories of the fingers which have to be executed and adapts it to the real contact pressure of each finger in order to guarantee that undesired slippage or contact?breaking is avoided during the execution of the manipulation task. This algorithm has been applied not only for the manipulation of normal rigid bodies but also for bodies whose centre of mass can be changed during the execution of the manipulation task.
Juan Antonio Corrales Ramo?n
Sustainable System Management with Fisher Information based Objectives
Sustainable ecosystem management that integrates ecological, economic and social perspectives is a complex task where simultaneous persistence of human and natural components of the system must be ensured. Given the complexity of this task, systems theory approaches based on soun...
Buried object location based on frequency-domain UWB measurements
International Nuclear Information System (INIS)
In this paper, a wideband ground penetrating radar (GPR) system and a proposed frequency-domain data analysis technique are presented for the detection of shallow buried objects such as anti-personnel landmines. The GPR system uses one transmitting antenna and an array of six monopole receiving antenna elements and operates from 1 GHz to 20 GHz. This system is able to acquire, save and analyse data in the frequency domain. A common source or wide-angle reflection and refraction technique has been used for acquiring and processing the data. This technique is effective for the rejection of ground surface clutter. By applying the C-scan scheme, metallic and plastic mine-like targets buried in dry soil will be located
Objective Pathological Voice Quality Assessment Based on HOS Features
This work proposes new features to improve the pathological voice quality classification performance. They are the means, the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal, grade 1, grade 2, and grade 3 voices classified in the GRBAS scale. The jitter, the shimmer, the harmonic-to-noise ratio (HNR), and the variance of the short-time energy are utilized as the conventional features. The performances are measured by the classification and regression tree (CART) method. Specifically, the CART-based method by utilizing both the conventional features and the HOS-based ones shows its effectiveness in the pathological voice quality measurement, with the classification accuracy of 87.8%.
Lee, Ji-Yeoun; Jeong, Sangbae; Choi, Hong-Shik; Hahn, Minsoo
Tracking object's type changes with fuzzy based fusion rule
In this paper the behavior of three combinational rules for temporal/sequential attribute data fusion for target type estimation are analyzed. The comparative analysis is based on: Dempster's fusion rule proposed in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5 (PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion rule, connecting the combination rules for information fusion with particular fuzzy operators, focusing on the t-norm based Conjunctive rule as an analog of the ordinary conjunctive rule and t-conorm based Disjunctive rule as an analog of the ordinary disjunctive rule. The way how different t-conorms and t-norms functions within TCN fusion rule influence over target type estimation performance is studied and estimated.
Tchamova, Albena; Smarandache, Florentin
Directory of Open Access Journals (Sweden)
Full Text Available Directing attention to a specific feature of an object has been linked to different forms of attentional modulation. Object-based attention theory founds on the finding that even task-irrelevant features at the selected object are subject to attentional modulation, while feature-based attention theory proposes a global processing benefit for the selected feature even at other objects. Most studies investigated either the one or the other form of attention, leaving open the possibility that both object- and feature-specific attentional effects do occur at the same time and may just represent two sides of a single attention system. We here investigate this issue by testing attentional spreading within and across objects, using reaction time measurements to changes of attended and unattended features on both attended and unattended objects. We asked subjects to report color and speed changes occurring on one of two overlapping random dot patterns, presented at the center of gaze. The key property of the stimulation was that only one of the features (e.g. motion direction was unique for each object, whereas the other feature (e.g. color was shared by both. The results of two experiments show that co-selection of unattended features even occurs when those features have no means for selecting the object. At the same time, they demonstrate that this processing benefit is not restricted to the selected object but spreads to the task-irrelevant one. We conceptualize these findings by a 3-step model of attention that assumes a task-dependent top-down gain, object-specific feature selection based on task- and binding characteristics, and a global feature-specific processing enhancement. The model allows for the unification of a vast amount of experimental results into a single model, and makes various experimentally testable predictions for the interaction of object- and feature-specific processes.
OBEST: The Object-Based Event Scenario Tree Methodology
International Nuclear Information System (INIS)
rlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies
Line fitting based feature extraction for object recognition
Image feature extraction plays a significant role in image based pattern applications. In this paper, we propose a new approach to generate hierarchical features. This new approach applies line fitting to adaptively divide regions based upon the amount of information and creates line fitting features for each subsequent region. It overcomes the feature wasting drawback of the wavelet based approach and demonstrates high performance in real applications. For gray scale images, we propose a diffusion equation approach to map information-rich pixels (pixels near edges and ridge pixels) into high values, and pixels in homogeneous regions into small values near zero that form energy map images. After the energy map images are generated, we propose a line fitting approach to divide regions recursively and create features for each region simultaneously. This new feature extraction approach is similar to wavelet based hierarchical feature extraction in which high layer features represent global characteristics and low layer features represent local characteristics. However, the new approach uses line fitting to adaptively focus on information-rich regions so that we avoid the feature waste problems of the wavelet approach in homogeneous regions. Finally, the experiments for handwriting word recognition show that the new method provides higher performance than the regular handwriting word recognition approach.
Li, Bing
Object-based change detection for landslide monitoring based on SPOT imagery
The steadily increasing availability of Earth observation (EO) data from a wide range of sensors facilitates the long-time monitoring of mass movements and retrospective analysis. Pixel-based approaches are most commonly used for detecting changes based on optical remote sensing data. However, single pixels are not suitable for depicting natural phenomena such as landslides in their full complexity and their transformation over time. By applying semi-automated object-based change detection limitations inherent to pixel-based methods can be overcome to a certain extent. For instance, the problem of variant spectral reflectance for the same pixel location in images from different points in time can be minimized. Therefore, atmospheric and radiometric correction of input data sets - although highly recommended - seems to be not that important for developing a straightforward change detection approach based on object-based image analysis (OBIA). The object-based change detection approach was developed for a subset of the Baichi catchment, which is located in the Shihmen Reservoir watershed in northern Taiwan. The study area is characterized by mountainous terrain with steep slopes and is regularly affected by severe landslides and debris flows. Several optical satellite images, i.e. SPOT images from different years and seasons with a spatial resolution ranging from 2.5 to 6.25 m, have been used for monitoring the past evolution of landslides and landslide affected areas. A digital elevation model (DEM) with 5 m spatial resolution was integrated in the analysis for supporting the differentiation of landslides and debris flows. The landslide changes were identified by comparing feature values of segmentation-derived image objects between two subsequent images in eCognition (Trimble) software. To increase the robustness and transferability of the approach we identified changes by using the relative difference in values of band-specific relational features, spectral indices and texture instead of by applying absolute spectral thresholds. Especially the Normalized Differenced Vegetation Index (NDVI) turned out to be useful as indicator for change. In this course, recent landslides can be differentiated from already existing mass movements. Furthermore, old landslides, which are already overgrown by vegetation, can be identified as well as reactivated ones. The presented approach can be applied for the regular update of existing landslide inventory maps or for the identification of areas that are potentially susceptible to landslides by analyzing the frequency of landslide events in the past. This might be of interest for decision makers and local stakeholders, as this kind of information can serve as useful input for disaster prevention and risk analysis.
Hölbling, Daniel; Friedl, Barbara; Eisank, Clemens
Condition-based maintenance - objective and possibilities of implementation
International Nuclear Information System (INIS)
The concept of condition-based maintenance inspires hopes for the responsibilities of saving extensive and high-productive basic funds which in particular build upon of means of technical diagnostics. The authors endeavour to realistically evaluate the recent state and foreseeable development of the possibilities of diagnosis. They make clear that condition-based maintenance is effective for most of the assemblies of a power plant unit. Since the concept to aggregate single actions of maintenance to larger groups in their periodical implementation cannot be left, the overall benefit is only realized in a comprehensive strategy for the units. Substantial effects are seen in observing damage evolution for planning and performance of maintenance actions and early detection of actual failures to prevent subsequent ones. (author)
Relational and Object-Oriented Methodology in Data Bases Systems
Directory of Open Access Journals (Sweden)
Full Text Available Database programming languages integrate concepts of databases and programming languages to provide both implementation tools for data-intensive applications and high-level user interfaces to databases. Frequently, database programs contain a large amount of application knowledge which is hidden in the procedural code and thus difficult to maintain with changing data and user views. This paper presents a first attempt to improve the situation by supporting the integrated definition and management of data and rules based on a setoriented and predicative approach. The use of database technology for integrated fact and rule base management is shown to have some important advantages in terms of fact and rule integrity, question-answering, and explanation of results.
Marian Pompiliu CRISTESCU
Object-based high contrast travel time tomography
Digital Repository Infrastructure Vision for European Research (DRIVER)
We consider travel time tomography problems involving detection of high contrast, discrete high velocity structures. This results in a discrete nonlinear inverse problem, for which traditional grid-based models and iterative linearized least-squares reconstruction algorithms are not suitable. This is because travel paths change significantly near the high contrast velocity structure, making it more difficult to inversely calculate the travel path and infer the velocity along...
Lin, Yenting; Ortega, Antonio
Directory of Open Access Journals (Sweden)
Full Text Available Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs and accelerated (reduced time to market development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low of procedure oriented Java based (object oriented software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.
Surbhi Maggo
From neural-based object recognition toward microelectronic eyes
Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.
Sheu, Bing J.; Bang, Sa Hyun
Novel 3-D Object Recognition Methodology Employing a Curvature-Based Histogram
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Full Text Available In this paper, a new object recognition algorithm employing a curvature-based histogram is presented. Recognition of three-dimensional (3-D objects using range images remains one of the most challenging problems in 3-D computer vision due to its noisy and cluttered scene characteristics. The key breakthroughs for this problem mainly lie in defining unique features that distinguish the similarity among various 3-D objects. In our approach, an object detection scheme is developed to identify targets underlining an automated search in the range images using an initial process of object segmentation to subdivide all possible objects in the scenes and then applying a process of object recognition based on geometric constraints and a curvature-based histogram for object recognition. The developed method has been verified through experimental tests for its feasibility confirmation.
Liang-Chia Chen
Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation
Digital Repository Infrastructure Vision for European Research (DRIVER)
Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object by compensating for the Doppler shift associated with the object movement. Numerical simulations and experiments indicate that by compensating for the Doppler shift, the system can accurately...
Slamet Widodo; Tomoo Shiigi; Naoki Hayashi; Hideo Kikuchi; Keigo Yanagida; Yoshiaki Nakatsuchi; Yuichi Ogawa; Naoshi Kondo
A Review of Computer Vision based Algorithms for accurate and efficient Object Detection
Directory of Open Access Journals (Sweden)
Full Text Available In this paper two vision-based algorithms are adopted to locate and identify the objects and obstacles from the environment. In recent days, robot vision and navigation are emerging as essential services especially in hazardous environments. In this work, two vision based techniques such as color based thresholding and template matching – both correlation based similarity measure and FFT (Fast Fourier Transform based have been adopted and used for object identification and classification using the image captured in CCD camera attached to the robotic arm. Then, the robotic arm manipulator is integrated with the computer for futher manipulation of the objects based on the application.
Aneissha Chebolu
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Full Text Available Object oriented languages usually avoid direct message passing, due to its complicated implementation, though that is the promising way to communicate in concurrently inherited objects. With the advancement in the high performance computing system, interaction between parallel application objects onto physical cores becomes one of the significant issues, which is not fully explored yet. In object oriented programming attribute data is included in objects and their state can be changed using the methods. Objects enable massage passing to other objects interacting with each other. Comprehensive problems can be molded by object-oriented methodology, and solves difficult program running object-oriented programs.Cores communicate with each other through communicator and groups in MPI, but in our reference architecture TBHIN (Triplet Based Hierarchical Interconnection Network, the cores are already faction in Triplets. We propose IITOIM Model to improve the performance with efficient intra-inter triplet cores communication mechanism between the objects in TBHIN
Shahnawaz Talpur
An object-oriented feature-based design system face-based detection of feature interactions
International Nuclear Information System (INIS)
This paper presents an object-oriented, feature-based design system which supports the integration of design and manufacture by ensuring that part descriptions fully account for any feature interactions. Manufacturing information is extracted from the feature descriptions in the form of volumes and Tool Access Directions, TADs. When features interact, both volumes and TADs are updated. This methodology has been demonstrated by developing a prototype system in which ACIS attributes are used to record feature information within the data structure of the solid model. The system implemented in the C++ programming language and embedded in a menu-driven X-windows user interface to the ACIS 3D Toolkit. (author)
Memory-based multiagent coevolution modeling for robust moving object tracking.
The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods. PMID:23843739
Wang, Yanjiang; Qi, Yujuan; Li, Yongping
Object-based saccadic selection during scene perception: evidence from viewing position effects.
The goal of the present study was to further test the hypothesis that objects are important units of saccade targeting and, by inference, attentional selection in real-world scene perception. To this end, we investigated where people fixate within objects embedded in natural scenes. Previously, we reported a preferred viewing location (PVL) close to the center of objects (Nuthmann & Henderson, 2010). Here, we qualify this basic finding by showing that the PVL is affected by object size and the distance between the object and the previous fixation (i.e., launch site distance). Moreover, we examined how within-object fixation position affected subsequent eye-movement behavior on the object. Unexpectedly, there was no refixation optimal viewing position (OVP) effect for objects in scenes. Where viewers initially placed their eyes on an object did not affect the likelihood of refixating that object, suggesting that some refixations on objects in scenes are made for reasons other than insufficient visual information. A fixation-duration inverted-optimal viewing (IOVP) effect was found for large objects: Fixations located at object center were longer than those falling near the edges of an object. Collectively, these findings lend further support to the notion of object-based saccade targeting in scenes. PMID:23547104
Pajak, Maciej; Nuthmann, Antje
A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation
International Nuclear Information System (INIS)
A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach
A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation
A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach.
Li, Guoli; Song, Gang; Wu, Yican; Zhang, Jian; Wang, Qunjing
Introducing AN Agent-Based Object Recognition Operator for Proximity Analysis
Object selection is a basic procedure in a Geographic Information System (GIS). Most current methods for doing so, select objects in two phases: create a simple distance-bounded geometric buffer; and intersect it with available features. This paper introduces a novel and intelligent selection operator based on the autonomy of the agent-based approach. The proposed operator recognizes objects around one object only in one step. In the proposed approach, each point object acts as an agent-automata object. It then senses its vicinity and identifies the surrounding objects. To assess the proposed model, the operator is designed, implemented, and evaluated in a case study. Finally, the results are evaluated and presented in details in the paper.
Behzadi, S.; Ali. Alesheikh, A.
Object Classification based Context Management for Identity Management in Internet of Things
DEFF Research Database (Denmark)
As computing technology is becoming more tightly coupled into dynamic and mobile world of the Internet of Things (IoT), security mechanism is more stringent, flexible and less intrusive. Scalability issue in IoT makes identity management (IdM) of ubiquitous objects more challenging, and there is a need of context-aware access control solution for IdM. Confronting uncertainty of different types of objects in IoT is not easy. This paper presents the logical framework for object classification in context aware IoT, as richer contextual information creates an impact on the access control. This paper proposes decision theory based object classification to provide contextual information and context management. Simulation results show that the proposed object classification is useful to improve network lifetime. Results also give motivation of object classification in terms of energy consumption. This paper also presents proof of concept and time analysis of the proposed decision theory based object classification.
Mahalle, Parikshit N.; Prasad, Neeli R.
Perceptual and motor-based responses to hand actions on objects: evidence from ERPs.
We carried out a study examining the electrophysiological responses when participants made object decisions to objects and non-objects subject to congruent and incongruent hand-grip actions. Despite the grip responses being irrelevant to the task, event-related potentials were sensitive to the handgrip. There were effects of grip congruency on both P1 and N1 components, over both posterior and motor cortices, with the effects emerging most strongly for familiar objects. In addition, enhanced lateralized readiness potentials were observed for incongruent grips. The results suggest that there are increased perceptual and motor-based responses to objects and object-like stimuli that are grasped correctly, even when the grip is irrelevant to the task. This is consistent with the automatic coding of potential appropriate actions based on visual information from objects in the environment. PMID:22644235
Kumar, Sanjay; Yoon, Eun Young; Humphreys, Glyn W
Late electrophysiological modulations of feature-based attention to object shapes.
Feature-based attention has been shown to aid object perception. Our previous ERP effects revealed temporally late feature-based modulation in response to objects relative to motion. The aim of the current study was to confirm the timing of feature-based influences on object perception while cueing within the feature dimension of shape. Participants were told to expect either "pillow" or "flower" objects embedded among random white and black lines. Participants more accurately reported the object's main color for valid compared to invalid shapes. ERPs revealed modulation from 252-502?ms, from occipital to frontal electrodes. Our results are consistent with previous findings examining the time course for processing similar stimuli (illusory contours). Our results provide novel insights into how attending to features of higher complexity aids object perception presumably via feed-forward and feedback mechanisms along the visual hierarchy. PMID:24423181
Stojanoski, Bobby Boge; Niemeier, Matthias
Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.
Fernandez Galarreta, J.; Kerle, N.; Gerke, M.
Model-based recognition of 3-D objects by geometric hashing technique
International Nuclear Information System (INIS)
A model-based object recognition system is developed for recognition of polyhedral objects. The system consists of feature extraction, modelling and matching stages. Linear features are used for object descriptions. Lines are obtained from edges using rotation transform. For modelling and recognition process, geometric hashing method is utilized. Each object is modelled using 2-D views taken from the viewpoints on the viewing sphere. A hidden line elimination algorithm is used to find these views from the wire frame model of the objects. The recognition experiments yielded satisfactory results. (author). 8 refs, 5 figs
Object-based Conditional Random Fields for Road Extraction from Remote Sensing Image
To make full use of spatially contextual information and topological information in the procedure of Object-based Image Analysis (OBIA), an object-based conditional random field is proposed and used for road extraction. Objects are produced with an initial segmentation, then their neighbours are constructed. Each object is represented by three kinds of features, including the colour, the gradient of histogram and the texture. Formulating the road extraction as a binary classification problem, a Conditional Random Fields model learns and is used for inference. The experimental results demonstrate that the proposed method is effective.
Huang, Zhijian; Xu, Fanjiang; Lu, Lei; Nie, Hongshan
Method for object motion characteristic estimation based on wavelet Multi-Resolution Analysis: MRA
Directory of Open Access Journals (Sweden)
Full Text Available Method for object motion characteristic estimation based on wavelet Multi-Resolution Analysis: MRA is proposed. With moving pictures, the motion characteristics, direction of translation, roll/pitch/yaw rotations can be estimated by MRA with an appropriate support length of the base function of wavelet. Through simulation study, method for determination of the appropriate support length of Daubechies base function is clarified. Also it is found that the proposed method for object motion characteristics estimation is validated.
Kohei Arai
Improved frame differencing based moving object detection using feet-step sound
Moving objects have been detected using various object detection techniques. Two categories for moving object detection techniques are frame differencing based and background subtraction based. These techniques are limited by camera scene complexity, light conditions, video type etc. Frame differencing based techniques process videos faster compared to background subtraction based techniques. Frame differencing based techniques detects only the boundary of the moving object and may fail for slow moving objects. These techniques for moving object detection can be improved by using sound data as most video recording cameras are equipped with a microphone. Sounds from human footsteps can be recorded with video and used with frame differencing techniques to improve moving object detection results. Camera microphones also record background noise with other background sound. This noisy data has been filtered out using the Fourier transform. When peak locations for each footstep sound are determined, and a Full Width at Half Maxima is computed for each peak, the number of frames within this width are counted, these frames are verify the presence of a moving object.
Roshan, Aditya; Zhang, Yun
Study on Objective Integrated Control of New Energy Power Projects Based on Reliability Theory
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Full Text Available Based on the research status of objective control theory of new energy power projects, analysed the system components of power projects, proposed the subsystem reliability control theory directed at four objectives, gave reliability control standards and calculation methods of four objectives, obtained the objective integrated method of subsystem reliability, used disjoint minimal path sets method to deal with the minimal path sets in the project construction process, proposed system reliability control theory of new energy power projects, then combined the known reliability control standards to assess project reliability, finally established objective integrated control model of new energy power projects based on reliability theory. Finally an simple example proves that the proposed objective integrated control model is simple and practical.
Yunna Wu
Real-Time Occlusion Handling in Augmented Reality Based on an Object Tracking Approach
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Full Text Available To produce a realistic augmentation in Augmented Reality, the correct relative positions of real objects and virtual objects are very important. In this paper, we propose a novel real-time occlusion handling method based on an object tracking approach. Our method is divided into three steps: selection of the occluding object, object tracking and occlusion handling. The user selects the occluding object using an interactive segmentation method. The contour of the selected object is then tracked in the subsequent frames in real-time. In the occlusion handling step, all the pixels on the tracked object are redrawn on the unprocessed augmented image to produce a new synthesized image in which the relative position between the real and virtual object is correct. The proposed method has several advantages. First, it is robust and stable, since it remains effective when the camera is moved through large changes of viewing angles and volumes or when the object and the background have similar colors. Second, it is fast, since the real object can be tracked in real-time. Last, a smoothing technique provides seamless merging between the augmented and virtual object. Several experiments are provided to validate the performance of the proposed method.
Yuan Tian
Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation
Directory of Open Access Journals (Sweden)
Full Text Available Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object by compensating for the Doppler shift associated with the object movement. Numerical simulations and experiments indicate that by compensating for the Doppler shift, the system can accurately determine the position of an object moving along a non-linear path. When the object moved in a circular path with an angular velocity of 0 to 1.3 rad/s, it could be localized to within 25 mm of the actual position. Experiments also showed the proposed system has a high noise tolerance of up to ?25 dB signal-to-noise ratio (SNR without compromising accuracy.
Slamet Widodo
Object detection based on template matching through use of best-so-far ABC.
Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution. PMID:24812556
Banharnsakun, Anan; Tanathong, Supannee
Moving Object Classification Method Based on SOM and K-means
Digital Repository Infrastructure Vision for European Research (DRIVER)
We do research on moving object classification in traffic video. Our aim is to classify the moving objects into pedestrians, bicycles and vehicles. Due to the advantage of self-organizing feature map (SOM), an unsupervised learning algorithm, which is simple and self organization, and the common usage of K-means clustering method, this paper combines SOM with K-means to do classification of moving objects in traffic video, constructs a system including four parts, and proposes a method based ...
Zhi-ming Cui; Jian-ming Chen; Jie Xia; Jian Wu
Detection and recognition of objects on waters' surfaces based on color elimination
Digital Repository Infrastructure Vision for European Research (DRIVER)
This thesis covers the issues of detection and recognition of objects on waters' surfaces based on color elimination. The goal of doctoral thesis is to devise a system that enables us detection and recognition of objects on waters' surfaces in the vicinity of energy facilities, hydro power plants. Such a system allows us to detect risks of objects, that represent a life danger, vessels, sport activities in this area such as boating, rafting, swimming, driving with motorboats and others on...
S?tricelj, Ales?
Technique of identifying speedy hyperspectral images object based on spectrum repository
International Nuclear Information System (INIS)
Aimed at the imported airborne hyperspectral data collection system (CASI/SASI), the authors first introduced the knowledge based spectral repository for multi ground objects, the study on the technique approaches to identifying object at speed with hyperspectral images, and then expound the principle of object identification, the flow of data processing and the programming procedure with open code, finally make a brief analysis for the results and its application. (authors)
Towards time-critical collision detection for deformable objects based on reduced models
Digital Repository Infrastructure Vision for European Research (DRIVER)
Topics of physically based modeling of deformable objects and collision detection have been extensively researched. Nowadays, the combination of GPU techniques and multiresolution physical models allows interactive simulations of complex deformable objects with a large number of polygons. To achieve this, the geometry of the object is separated from the deformable model in order to represent the latter at different levels of resolutions (reduced models). Recently...
Mendoza, Cesar; O Sullivan, Carol Ann
Semantic Predicate Types and Approximation for Class-based Object Oriented Programming
Digital Repository Infrastructure Vision for European Research (DRIVER)
We apply the principles of the intersection type discipline to the study of class-based object oriented programs and; our work follows from a similar approach (in the context of Abadi and Cardelli's Varsigma-object calculus) taken by van Bakel and de'Liguoro. We define an extension of Featherweight Java, FJc and present a predicate system which we show to be sound and expressive. We also show that our system provides a semantic underpinning for the object oriented paradigm b...
Bakel, Steffen; Rowe, Reuben N. S.
Object tracking system using a VSW algorithm based on color and point features
Directory of Open Access Journals (Sweden)
Full Text Available Abstract An object tracking system using a variable search window (VSW algorithm based on color and feature points is proposed. A meanshift algorithm is an object tracking technique that works according to color probability distributions. An advantage of this algorithm based on color is that it is robust to specific color objects; however, a disadvantage is that it is sensitive to non-specific color objects due to illumination and noise. Therefore, to offset this weakness, it presents the VSW algorithm based on robust feature points for the accurate tracking of moving objects. The proposed method extracts the feature points of a detected object which is the region of interest (ROI, and generates a VSW using the given information which is the positions of extracted feature points. The goal of this paper is to achieve an efficient and effective object tracking system that meets the accurate tracking of moving objects. Through experiments, the object tracking system is implemented that it performs more precisely than existing techniques.
Lim Hye-Youn
A Fast Feature Points-Based Object Tracking Method for Robot Grasp
Directory of Open Access Journals (Sweden)
Full Text Available In this paper, we propose a fast feature points?based object tracking method for robot grasp. In the detection phase, we detect the object with SIFT feature points extraction and matching. Then we compute the object's image position with homography constraints and set up an interest window to accommodate the object. In the tracking phase, we only focus on the interest window, detecting feature points from the window and updating the window's position and size. Our method is of special practical meaning in the case of service robot grasp. Because when the robot grasps the object, the object's image size is usually small relative to the whole image, it is unnecessary to detect the whole image. On the other hand, the object is partially occluded by the robot gripper. SIFT is good at dealing with occlusion, but it is time consuming. Hence, by combining SIFT and an interest window, our method gains the ability to deal with occlusion and can satisfy the real?time requirements at the same time. Experiments show that our method exceeds several leading feature points?based object tracking methods in real?time performance.
Yang Yang
Directory of Open Access Journals (Sweden)
Full Text Available This paper proposes a probabilistic model of object category learning in conjunction with attention-guided organized perception. This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on dynamically-formed Markov random fields around salient preattentive points and co-occurring segments are grouped in the neighborhood of selective attended segments. In object category learning, a set of classes of each object category is obtained based on the probabilistic latent component analysis with the variable number of classes from bags of features of segments extracted from images which contain the categorical objects in context and an object category is represented by a composite of object classes. Through experiments using two image data sets, it is shown that the model learns a probabilistic structure of intra-categorical composition and inter-categorical difference of object categories and achieves high performance in object category recognition.
Masayasu Atsumi
View-Based Models of 3D Object Recognition and Class-Specific Invariance.
This paper describes the main features of a view-based model of object recognition. The model tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network in which eac...
N. K. Logothetis, T. Vetter, A. Hurlbert, T. Poggio
The Southern Illinois University School of Medicine applies mastery learning concepts to an objectives-based curriculum and uses the Nedelsky method to assess mastery of core objectives. Provisions are made for remediating deficiences. The Nedelsky technique is used for determining an acceptable level of performance in multiple choice examinations…
Paiva, Rosalia E. A.; Vu, Nu Viet
A knowledge-based object recognition system for applications in the space station
A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.
Dhawan, Atam P.
Object Tracking in Rotational-based and Regular Deployment Visual Sensor Networks
Directory of Open Access Journals (Sweden)
Full Text Available Visual sensor networks offer surveillance applications, particularly object tracking. This study uses a rotational-based and regular deployment visual sensor network to track an object. The proposed method is applied to detect the object tracking in security monitoring. This study provides two types of network architecture to deploy the sensor nodes and utilizes the lines of sight between cameras to form a defense face to surround the mobile object. Each sensor node has rotational camera lens and is deployed to form regular network architecture. The proposed tracking method assigns the sensor nodes to surround by an object and to provide continuous monitoring for the object. The proposed update method can ensure that the object is tracking by an update defense face, even if the object is moving out of the original defense face. The major advantage of this algorithm that is can solve the lost object location problem of object tracking. Finally, this study utilizes simulations to analyse and estimate the efficacy and the performance of object tracking in the proposed network architecture.
Hua-Wen Tsai
An Ada-based preprocessor language for concurrent object oriented programming
International Nuclear Information System (INIS)
In this paper, implementation issues of concurrent-objected programming using Ada 95 are addressed. Ada is not a pure object-oriented language; in order to make it so, a uniform template for structuring object classes is proposed. The template constitutes a basis for an Ada-based preprocessor language that handles concurrent object-oriented programming. The preprocessor accepts Ada-like object-oriented programs (object classes, subclasses and main program) as input and produces Ada 95 concurrent object-oriented program units as output. The preprocessor language has the advantage of adding a new component to the class specification called the protocol, which specifies the order for requesting methods f an object. The preprocessor also touches on the extensibility of object classes issue. It supports defining class hierarchies by inheritance and aggregation. In addition, the preprocessor language supports the re-use of Ada packages, which are not necessarily written according to the object-oriented approach. The paper also investigates the definition of circular dependent object classes and proposes a solution for introducing a collection of classes. (author)
Object classification methods for application in FPGA based vehicle video detector
Directory of Open Access Journals (Sweden)
Full Text Available The paper presents a discussion of properties of object classification methods utilized in processing video streams from a camera. Methods based on feature extraction, model fitting and invariant determination are evaluated. Petri nets are used for modelling the processing flow. Data objects and transitions are defined which are suitable for efficient implementation in FPGA circuits. Processing characteristics and problems of the implementations are shown. An invariant based method is assessed as most suitable for application in a vehicle video detector.
Wies?aw PAMU?A
Lossy to lossless object-based coding of 3-D MRI data
Digital Repository Infrastructure Vision for European Research (DRIVER)
We propose a fully three-dimensional object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the diffe...
Menegaz, Gloria; Thiran, Jean-philippe
Concept of development of integrated computer - based control system for 'Ukryttia' object
International Nuclear Information System (INIS)
The structural concept of Chernobyl NPP 'Ukryttia' Object's integrated computer - based control system development is presented on the basis of general concept of integrated Computer - based Control System (CCS) design process for organizing and technical management subjects.The concept is aimed at state-of-the-art architectural design technique application and allows using modern computer-aided facilities for functional model,information (logical and physical) models development,as well as for system object model under design
A Step Forward To Component-based Software Cost Estimation in Object-oriented Environment
Digital Repository Infrastructure Vision for European Research (DRIVER)
Software cost estimation (SCE) of a project is pivotal to the acceptance or rejection of the development of software project. Various SCE techniques have been in practice with their own strengths and limitations. The latest of these is object-oriented one. Currently object-oriented approach for SCE is based on Line of Code (LOC), function points, functions and classes etc. Relatively less attention has been paid to the SCE in component-based software engineering (CBSE). So t...
Ahmed, Nadeem; Asim, M. Rafiq; Qureshi, M. Rizwan Jameel
Online decoding of object-based attention using real-time fMRI.
Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for moment-to-moment decoding of attention to spatially overlapped objects belonging to two different object categories. First, a whole-brain classifier was trained on pictures of faces and places. Subjects then saw transparently overlapped pictures of a face and a place, and attended to only one of them while ignoring the other. The category of the attended object, face or place, was decoded on a scan-by-scan basis using the previously trained decoder. The decoder performed at 77.6% accuracy indicating that despite competing bottom-up sensory input, object-based visual attention biased neural patterns towards that of the attended object. Furthermore, a comparison between different classification approaches indicated that the representation of faces and places is distributed rather than focal. This implies that real-time decoding of object-based attention requires a multivariate decoding approach that can detect these distributed patterns of cortical activity. PMID:24438492
Niazi, Adnan M; van den Broek, Philip L C; Klanke, Stefan; Barth, Markus; Poel, Mannes; Desain, Peter; van Gerven, Marcel A J
An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks
Digital Repository Infrastructure Vision for European Research (DRIVER)
In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is p...
A New Control Structure Model Based on Object-oriented Petri Nets
Directory of Open Access Journals (Sweden)
Full Text Available Petri nets in object-oriented modeling, based on the objects introduced a special internal changes - the control changes, the introduction of objects in the controller, a control structure based on object-oriented Petri nets (CS-OOPN model, and described the CS-OOPN modeling steps of the described of CS-OOPN modeling. The model overcomes the traditional object-oriented Petri nets modeling of flexible processes and systems lack the flexibility of the shortcomings can be more intuitive, flexible to describe the work flow. Finally, using this model for a group management system and for modeling equipment procurement, and approval departments CS-OOPN model as an example, find its associated matrix, tree coverage and P-invariant, the correlation analysis to prove that building the model has good performance and to meet the system requirements change and restructuring.
Yan-pei Liu
International Nuclear Information System (INIS)
In the present work, we used the (CR-LR) difference technique for retrospective radon progeny measurements in 17 dwellings based on implanted 210Po activities in glass objects. A total of 48 glass objects were examined, but only 19 objects gave results which were sufficiently reliable due to the sensitivity of the method. From these 19 data, an increase in the surface 210Po activities in the glass objects with the age of the glass objects was noticeable as expected. The surface activities of 210Po in the glass objects were then converted to the potential alpha energy concentration (PAEC) through a calibration curve. It was found that the PAEC for dwelling sites did not change significantly with the building age
An Automatic Algorithm for Object Recognition and Detection Based on Asift Keypoints
Directory of Open Access Journals (Sweden)
Full Text Available Object recognition is an important task in image processing and computer vision. This paper presents aperfect method for object recognition with full boundary detection by combining affine scale invariantfeature transform (ASIFT and a region merging algorithm. ASIFT is a fully affine invariant algorithm that means features are invariant to six affine parameters namely translation (2 parameters, zoom, rotation and two camera axis orientations. The features are very reliable and give us strong keypoints that can be used for matching between different images of an object. We trained an object in several images with different aspects for finding best keypoints of it. Then, a robust region merging algorithm is used torecognize and detect the object with full boundary in the other images based on ASIFT keypoints and a similarity measure for merging regions in the image. Experimental results show that the presented method is very efficient and powerful to recognize the object and detect it with high accuracy.
Reza Oji
Scientific Electronic Library Online (English)
Full Text Available SciELO Brazil | Language: English Abstract in english An approach based on concurrent object oriented programming (COOP) to build a control system for a mobile robot is presented. A behavior-based control system is decomposed in intercommunicating concurrent objects named Agents. These agents belong to five categories: Primitive Sensor, Virtual Sensor, [...] Behavior, Primitive Actuator and Virtual Actuator. Based on this approach, a C++ tool is developed, where the categories above are implemented as C++ classes, in which built-in communication mechanisms are included. Each class has a standard interface and functionality. It is possible, then, to develop a complex control system by deriving new classes from the base classes and by instantiating objects. These objects are interconnected in a dynamic manner and thus building a control system with different behavior levels, which is able to react to environment changes.
José Eduardo Mendonça, Xavier; Hansjörg Andreas, Schneebeli.
Scientific Electronic Library Online (English)
Full Text Available SciELO Brazil | Language: English Abstract in english An approach based on concurrent object oriented programming (COOP) to build a control system for a mobile robot is presented. A behavior-based control system is decomposed in intercommunicating concurrent objects named Agents. These agents belong to five categories: Primitive Sensor, Virtual Sensor, [...] Behavior, Primitive Actuator and Virtual Actuator. Based on this approach, a C++ tool is developed, where the categories above are implemented as C++ classes, in which built-in communication mechanisms are included. Each class has a standard interface and functionality. It is possible, then, to develop a complex control system by deriving new classes from the base classes and by instantiating objects. These objects are interconnected in a dynamic manner and thus building a control system with different behavior levels, which is able to react to environment changes.
José Eduardo Mendonça, Xavier; Hansjörg Andreas, Schneebeli.
Directory of Open Access Journals (Sweden)
Full Text Available In this paper an object recognition system using template matching is implemented. Since objects are represented by either an external or internal descriptors, a combination of using signature, principal component analysis and color is used. The system efficacy is measured by applying it to recognize an image of a chessboard with a set of objects (pieces. The output of the system includes the pieces names, locations and color. The signature feature is used to distinguish the pieces types based on their external shape but when it falls short, the principal components analysis is used instead. The object color is also obtained. The matching between features is carried out based on Euclidean distance metric .The proposed system is implemented, trained, and tested using Matlab based on a set of collected samples representing chessboard images. The simulation results show the effectiveness of the proposed method in recognizing the pieces locations, types, and color.
Inad A. Aljarrah
Directory of Open Access Journals (Sweden)
Full Text Available Object Modeling Technique is widely applied in the field of software engineering; and in this paper we applied this technique to model a mobile robot including its behaviors and interactions with environment. The paper first describes key background knowledge about object oriented analysis in software engineering, behavior based robotics and their similarities. Then, based on these similarities, the paper uses object oriented methods of software engineering, such as unified modeling language (UML, to analyze and model the architecture; and to design behaviors for a behavior-based robot, which is expected to wander with autonomous obstacle avoidance in unknown environment. Object oriented methods permit a translation from conceptual behavior models to computer programming representations, and separate concrete control algorithms from robot modeling. With this approach, the paper implements a fuzzy algorithm for obstacle avoidance behavior of the constructed behavior models in a physical robot, and made experiments in the given indoor environment.
Qian Zhang
Object-oriented multimedia database system with versioning and content-based retrieval
A prototype object-oriented multimedia database management system currently being developed is described. The system supports the storage and retrieval of images, video, audio and documents composed of these types. The major features of the system include: (1) content-based indexes for each of the data types, (2) an intuitive user-oriented query language based on these indexes, (3) manual, semi-automatic and automatic indexing modes, (4) object- based user data models incorporated in query processing, (5) image/audio/video processing incorporated in the system, (6) versioning of objects, (7) browsing and navigation facilities. The indexes are interval-based and describe spatio-temporal relations between pairs of objects in the respective media. The query processing mechanism is described, as is the object- oriented data modeling facility. The most innovative aspects of this work are the following: (1) extension of iconic indexing of images to the audio and video data types, (2) an embedding of content-based iconic indexing in a multimedia database management system with particular emphasis on user-oriented indexing and querying, (3) the use of an object-oriented data model to alleviate the aliasing problem in query formation, (4) versioning of images/audio/video to save storage space.
Arndt, Timothy; Guercio, Angela
User-assisted Object Detection by Segment Based Similarity Measures in Mobile Laser Scanner Data
This paper describes a method that aims to find all instances of a certain object in Mobile Laser Scanner (MLS) data. In a userassisted approach, a sample segment of an object is selected, and all similar objects are to be found. By selecting samples from multiple classes, a classification can be performed. Key assumption in this approach is that a one-to-one relationship exists between segments and objects. In this paper the focus is twofold: (1) to explain how to get proper segments, and (2) to describe how to find similar objects. Point attributes that help separating neighbouring objects are presented. These point attributes are used in an attributed connected component algorithm where segments are grown, based on proximity and attribute values. Per component, a feature vector is proposed that consists of two parts. The first is a height histogram, containing information on the height distribution of points within a component. The second contains size and shape information, based on the components' bounding box. A simple correlation function is used to find similarities between samples, as selected by a user, and other components. Our approach is tested on a MLS dataset, containing over 300 objects in 13 classes. Detection accuracies heavily depend on the success of the segmentation, and the number of selected samples in combination with the variety of object types in the scene.
Oude Elberink, S.; Kemboi, B.
A Study on Software-based Sensing Technology for Multiple Object Control in AR Video
Directory of Open Access Journals (Sweden)
Full Text Available Researches on Augmented Reality (AR have recently received attention. With these, the Machine-to-Machine (M2M market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms.
Seoksoo Kim
A study on software-based sensing technology for multiple object control in AR video.
Researches on Augmented Reality (AR) have recently received attention. With these, the Machine-to-Machine (M2M) market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker'should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms. PMID:22163444
Jung, Sungmo; Song, Jae-Gu; Hwang, Dae-Joon; Ahn, Jae Young; Kim, Seoksoo
LABRADOR: a learning autonomous behavior-based robot for adaptive detection and object retrieval
As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.
Yamauchi, Brian; Moseley, Mark; Brookshire, Jonathan
An Object-Oriented Architecture for a Web-Based CAI System.
This paper describes the design and implementation of an object-oriented World Wide Web-based CAI (Computer-Assisted Instruction) system. The goal of the design is to provide a flexible CAI/ITS (Intelligent Tutoring System) framework with full extendibility and reusability, as well as to exploit Web-based software technologies such as JAVA, ASP (a…
Nakabayashi, Kiyoshi; Hoshide, Takahide; Seshimo, Hitoshi; Fukuhara, Yoshimi
The pixel rectangle index used in object based building extraction from high resolution images
An improved high resolution object-based building extraction method based on Pixel Rectangle Index is presented in this paper. We use Minimum Span Tree optimal theory to realize object-based high resolution image segmentation. First, we proposed a rotation invariant Pixel Rectangle Index by introducing the principal direction of homogeneous area. Second, we improved the calculation of edge-weight by introducing the band-weight and Pixel Rectangle Index. The QuickBird high resolution images were used to do the building extraction experiment. The experiment result proved that this method can obtain high extraction accuracy and this algorithm can be efficiently used in remote sensing images.
Cui, W. H.; Feng, X.; Qin, K.
Investigation of a combined portable IR system for laser scanning land-based object
International Nuclear Information System (INIS)
Some problems of informative capacities of laser radar are considered. An analysis of specific properties realizing IR identification systems has been developed for land-based objects laser scanning. Some technical characteristics of the systems are mentioned, such as systems response speed, 3D imaging non-scanning by using 3D FPA detectors. The combined portable facilities as a whole present a joint structure Laser IR Scanner and IR irradiation detector for identifying land-based object. The estimation of channel capacity and delays in the synchronization system between base station and mobile station are given
3D object following based on visual information for Unmanned Aerial Vehicles
Digital Repository Infrastructure Vision for European Research (DRIVER)
This article presents a novel system and a control strategy for visual following of a 3D moving object by an Unmanned Aerial Vehicle UAV. The presented strategy is based only on the visual information given by an adaptive tracking method based on the color information, which jointly with the dynamics of a camera fixed to a rotary wind UAV are used to develop an Image-based visual servoing IBVS system. This system is focused on continuously following a 3D moving target object, maintaining it w...
Mondragon Bernal, Ivan Fernando; Campoy Cervera, Pascual; Olivares Me?ndez, Miguel A?ngel; Marti?nez Luna, Carol Viviana
Soft object deformation monitoring and learning for model-based robotic hand manipulation.
This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background. PMID:22207640
Cretu, Ana-Maria; Payeur, Pierre; Petriu, Emil M
Digital Repository Infrastructure Vision for European Research (DRIVER)
This paper deals with an approach to the optimization of enterprise information system (EIS) based on the object-based knowledge mesh (OKM) and binary tree. Firstly, to explore the optimization of EIS by the user's function requirements, an OKM expression representation based on the user's satisfaction and binary tree is proposed. Secondly, based on the definitions of the fuzzy function-satisfaction degree relationships on the OKM functions, the optimization model is constructed. ...
Chaogai Xue; Haiwang Cao
An object oriented framework of EPICS for MicroTCA based control system
International Nuclear Information System (INIS)
EPICS (Experimental Physics and Industrial Control System) is a distributed control system platform which has been widely used for large scientific devices control like particle accelerators and fusion plant. EPICS has introduced object oriented (C++) interfaces to most of the core services. But the major part of EPICS, the run-time database, only provides C interfaces, which is hard to involve the EPICS record concerned data and routines in the object oriented architecture of the software. This paper presents an object oriented framework which contains some abstract classes to encapsulate the EPICS record concerned data and routines in C++ classes so that full OOA (Objected Oriented Analysis) and OOD (Object Oriented Design) methodologies can be used for EPICS IOC design. We also present a dynamic device management scheme for the hot swap capability of the MicroTCA based control system. (authors)
Brazilian Proposal for Agent-Based Learning Objects Metadata Standard - OBAA
This paper presents the Agent Based Learning Objects - OBAA standard proposal. The main goal of the research was to establish a standardized specification of the technical and functional requirements of interoperable learning objects. In our context, interoperability regards the operation of the content inside Web, Digital TV and mobile environments, supporting accessibility and pedagogical issues. In this proposal it has been explored the convergence among the multi-agent systems, learning object and ubiquitous computing technologies, allowing the authoring, storage and recovery of learning object in varied contexts and through different digital platforms. The result of this research was the definition of the OBAA proposal containing the requirements, specifications and architectures that will compose the Brazilian standard for the management, transmission, storage, search, editing and use of interoperable learning object.
Vicari, Rosa Maria; Ribeiro, Alexandre; da Silva, Júlia Marques Carvalho; Santos, Elder Rizzon; Primo, Tiago; Bez, Marta
A Fast Object Tracking Approach Based on the Motion Vector in a Compressed Domain
Directory of Open Access Journals (Sweden)
Full Text Available Particle set sampling and weighting are both at the core of particle filter?based object tracking methods. Aiming to optimally represent the object?s motion state, a large amount of particles ? in the classical particle method ? is a prerequisite. The high?cost calculation of these particles significantly slows down the convergence of the algorithm. To this problem, a prior approach which originated from the process of video compressing and uncompressing is introduced to optimize the phase of particle sampling, making the collected particles centre on and cover the object region in the current image. This advantage dramatically reduces the number of particles required by the regularized particle sampling method, solving the problem of the high computational cost for tracking objects, while the performance of the algorithm is stable.
Hui-bin Wang
The study of high-resolution imaging of astronomical object based on phase-diversity method
The high resolution imaging of astronomical object based on phase-diversity method is a technique for obtaining estimates of both the object and the distribution of wavefront induced by atmospheric turbulence,by exploiting the simultaneous collection of one or more pairs of short-exposure images. One of the pair images is the conventional focal-plane image and another is formed by further blurring the focal-image by defocus.The telescopic optical system and image collection system of phase-diversity method are simulated by using computer in this paper. Based on signal estimation theory and optimization theory, the objective function is derived under additive Gaussian noise model. The resulting large scale unconstrained optimization problem is solved numerically using a limited memory BFGS method. The restoring results demonstrate that the phase-diversity method is remarkably efficient for removing the effect of atmospheric turbulence and solving the image restoration problem of astronomical extended object.
Li, Q.; Shen, M. Z.
Directory of Open Access Journals (Sweden)
Full Text Available ????????????????????????????????????????????????????????????????????5???"????????????Pareto????????"?????????????????????????????????????????????????????????????????????????Pareto??????????????????????????????????????Pareto??????????????????????????????????????????????????????????????????????????????????????????????????????????????????Distribution network reconfiguration can improve the operation security, economy and power qua- lity of distribution network, for the current national construction and application of distribution automation system it has great significance. This paper presents a multi-objective distribution network optimal reconfi- guration of the particle swarm algorithm which based on a niche technology, the introduction of the concept of Pareto optimal to achieve a true sense of the multi-objective optimization; apply the particle swarm algori- thm to achieve the search of the Pareto optimal solution set of multi-objective reconfiguration, using niche technology and mutation operators to maintain the population diversity and dispersion, improved particle swarm algorithm global convergence reliability and convergence speed. Theoretical analysis and numerical results show that: distribution network reconfiguration based on niche particle swarm optimization meet the requirements in the speed and accuracy, and have more practical significance than the single-objective op- timization.
An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Full Text Available In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs. Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time.
Frank Yeong-Sung Lin
Object-based attention involves the sequential activation of feature-specific cortical modules.
Object-based theories of attention propose that the selection of an object's feature leads to the rapid selection of all other constituent features, even those that are task irrelevant. We used magnetoencephalographic recordings to examine the timing and sequencing of neural activity patterns in feature-specific cortical areas as human subjects performed an object-based attention task. Subjects attended to one of two superimposed moving dot arrays that were perceived as transparent surfaces on the basis either of color or speed of motion. When surface motion was attended, the magnetoencephalographic waveforms showed enhanced activity in the motion-specific cortical area starting at ? 150 ms after motion onset, followed after ? 60 ms by enhanced activity in the color-specific area. When surface color was attended, this temporal sequence was reversed. This rapid sequential activation of the relevant and irrelevant feature modules provides a neural basis for the binding of an object's features into a unitary perceptual experience. PMID:24561999
Schoenfeld, Mircea A; Hopf, Jens-Max; Merkel, Christian; Heinze, Hans-Jochen; Hillyard, Steven A
LUGrid: Update-tolerant Grid-based Indexing for Moving Objects
Digital Repository Infrastructure Vision for European Research (DRIVER)
Indexing moving objects is a fundamental issue in spatiotemporal databases. In this paper, we propose an adaptive Lazy-Update Grid-based index (LUGrid, for short) that minimizes the cost of object updates. LUGrid is designed with two important features, namely, lazy insertion and lazy deletion. Lazy insertion reduces the update I/Os by adding an additional memory-resident layer over the disk index. Lazy deletion reduces update cost by avoiding deleting single obsolete entry immediately. Inste...
Xiong, Xiopeng; Mokbel, Mohamed; Aref, Walid G.
Pirus: A Web-based File Hosting Service with Object Oriented Logic in Cloud Computing
Digital Repository Infrastructure Vision for European Research (DRIVER)
In this paper a new Web-based File Hosting Service with Object Oriented Logic in Cloud Computing called Pirus was developed. The service will be used by the academic community of the University of Piraeus giving users the ability to remotely store and access their personal files with no security compromises. It also offers the administrators the ability to manage users and roles. The objective was to deliver a fully operational service, using state-of-the-art programming tec...
Kallergis, Dimitrios; Chimos, Konstantinos; Stefanos, Vizikidis; Karvounidis, Theodoros; Douligeris, Christos
Real world object based access to architecture learning material - the MACE experience
Digital Repository Infrastructure Vision for European Research (DRIVER)
The MACE project aims to support architecture students while searching for learning materials by offering advanced graphical metadata-based access to learning resources in architecture across repository boundaries. Therefore, the MACE system uses real world object representations which serve as connection between learning materials. This enables the students to explore new and more complete learning paths. In this paper we outline the generation and usage of real world object representations ...
Niemann, K.; Wolpers, M.
Directory of Open Access Journals (Sweden)
Full Text Available It is a well known fact that software maintenance plays a major role and finds importance in software development life cycle. As object - oriented programming has become the standard, it is very important to understand the problems of maintaining object -oriented software systems. This paper aims at evaluating object - oriented software system through change requirement traceability – based impact analysis methodology for non functional requirements using functional requirements . The major issues have been related to change impact algorithms and inheritance of functionality.
Sunil T. D
Presence of contaminants, such as gasoline, moisture, and coolant in the engine lubricant indicates mechanical failure within the engine and significantly reduces lubricant quality. This paper describes a novel sensing system, its methodology and experimental verifications for analysis of the presence of contaminants in the engine lubricants. The sensing methodology is based on the statistical shape analysis methodology utilizing optical analysis of the distortion effect when an object image is obtained through a thin random optical medium. The novelty of the proposed sensing system lies within the employed methodology which an object with a known periodic shape is introduced behind a thin film of the contaminated lubricant. In this case, an acquired image represents a combined lubricant-object optical appearance, where an a priori known periodical structure of the object is distorted by a contaminated lubricant. The object, e.g. a stainless steel woven wire cloth with a mesh size of 65×65 µm2 and a circular wire diameter of 33 µm was placed behind a microfluidic channel, containing engine lubricant and optical images of flowing lubricant with stationary object were acquired and analyzed. Several parameters of acquired optical images, such as, color of lubricant and object, object shape width at object and lubricant levels, object relative color, and object width non-uniformity coefficient, were proposed. Measured on-line parameters were used for optical analysis of fresh and contaminated lubricants. Estimation of contaminant presence and lubricant condition was performed by comparison of parameters for fresh and contaminated lubricants. Developed methodology was verified experimentally showing ability to distinguish lubricants with 1%, 4%, 7%, and 10% coolant, gasoline and water contamination individually and in a combination form of coolant (0%-5%) and gasoline (0%-5%).
Bordatchev, Evgueni; Aghayan, Hamid; Yang, Jun
Object Hierarchy-based Supervised Characterisation of Synthetic Aperture Radar Sensor Images
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Full Text Available A method of supervised characterisation of synthetic aperture radar (SAR satellite images has been discussed in which simple object shape features of satellite images have been used to classify and describe the terrain types. This scheme is based on a multilevel approach in which objects of interest are first segmented out from the image and subsequently characterised based on their shape features. Once all objects have been characterised, the entire image can be characterised. Emphasis has been laid on the hierarchical information extraction from the image which enables greater flexibility in characterising the image and is not restricted to mere classification. The paper also describes a method for giving relative importance among features, i.e., to give more weights to those features that are better than others in distinguishing between competing classes. A method of comparing two SAR sensor images based on terrain elements present in the images has also been described here.
Ish Rishabh
Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods
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Full Text Available Most of the spam filtering techniques are based on objective methods such as the content filtering and DNS/reverse DNS checks. Recently, some cooperative subjective spam filtering techniques are proposed. Objective methods suffer from the false positive and false negative classification. Objective methods based on the content filtering are time consuming and resource demanding. They are inaccurate and require continuous update to cope with newly invented spammer's tricks. On the other side, the existing subjective proposals have some drawbacks like the attacks from malicious users that make them unreliable and the privacy. In this paper, we propose an efficient spam filtering system that is based on a smart cooperative subjective technique for content filtering in addition to the fastest and the most reliable non-content-based objective methods. The system combines several applications. The first is a web-based system that we have developed based on the proposed technique. A server application having extra features suitable for the enterprises and closed work groups is a second part of the system. Another part is a set of standard web services that allow any existing email server or email client to interact with the system. It allows the email servers to query the system for email filtering. They can also allow the users via the mail user agents to participate in the subjective spam filtering problem.
Samir A. Elsagheer Mohamed
Directory of Open Access Journals (Sweden)
Full Text Available In this paper, an approach to integral color texture invariant information with a neural networkapproach to object recognition is proposed. A color-texture context for image retrieval system based onthe integral information of an image is represented as one compact representation base on colorhistogram approach. A general and efficient design approach using a neural classifier to cope with smalltraining sets of high dimension, which is a problem frequency encountered in object recognition, isfocused in this paper for general images. The proposed system is tested for various colored imagesamples and the recognition accuracy is evaluated.
G.Shyama Chandra Prasad
Directory of Open Access Journals (Sweden)
Full Text Available We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values in at least one band, with Peltophorum pterocarpum being most distinct from other species. Object-based approaches were consistently superior to pixel-based methods, which were particularly low in accuracy for tree species with small canopy sizes, such as Cocos nucifera and Roystonea regia. There was a strong and significant correlation between the number of trees determined on the ground and from object-based classification. Overall, object-based approaches appear capable of discriminating the six most common species in a challenging urban environment, with substantial heterogeneity of tree canopy sizes.
Harini Nagendra
Goal-directed attention alters the tuning of object-based representations in extrastriate cortex
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Full Text Available Humans survive in environments that contain a vast quantity and variety of visual information. All items of perceived visual information must be represented within a limited number of brain networks. The human brain requires mechanisms for selecting only a relevant fraction of perceived information for more in-depth processing, where neural representations of that information may be actively maintained and utilized for goal-directed behavior. Object-based attention is crucial for goal-directed behavior and yet remains poorly understood. Thus, in the study we investigate how neural representations of visual object information are guided by selective attention. The magnitude of activation in human extrastriate cortex has been shown to be modulated by attention; however object-based attention is not likely to be fully explained by a localized gain mechanism. Thus, we measured information coded in spatially distributed patterns of brain activity with fMRI while human participants performed a task requiring selective processing of a relevant visual object category that differed across conditions. Using pattern classification and spatial correlation techniques, we found that the direction of selective attention is implemented as a shift in the tuning of object-based information representations within extrastriate cortex. In contrast, we found that representations within lateral prefrontal cortex coded for the attention condition rather than the concrete representations of object category. In sum, our findings are consistent with a model of object-based selective attention in which representations coded within extrastriate cortex are tuned to favor the representation of goal-relevant information, guided by more abstract representations within lateral prefrontal cortex.
Review of holographic-based three-dimensional object recognition techniques [Invited].
With the advancement of computing and optical technologies, it is now possible to capture digital holograms of real-life object scenes. Theoretically, through the analysis of a hologram, the three-dimensional (3D) objects coded on the hologram can be identified. However, being different from an optical image, a hologram is composed of complicated fringes that cannot be analyzed easily with traditional computer vision methods. Over the years, numerous important research investigations have been attempted to provide viable solutions to this problem. The aim of this work is three-fold. First, we provide a quick walkthrough on the overall development of holographic-based 3D object recognition (H3DOR) in the past five decades, from film-based approaches to digital-based innovation. Second, we describe in more detail a number of selected H3DOR methods that are introduced at different timelines, starting from the late sixties and then from the seventies, where viable digital holographic-based 3D recognition methods began to emerge. Finally, we present our work on digital holographic, pose-invariant 3D object recognition that is based on a recently introduced virtual diffraction plane framework. As our method has not been reported elsewhere, we have included some experimental results to demonstrate the feasibility of the approach. PMID:25322141
Tsang, P W M; Poon, T-C; Liu, J-P; Situ, W C
A model-based conceptual clustering of moving objects in video surveillance
Data mining techniques have been applied in video databases to identify various patterns or groups. Clustering analysis is used to find the patterns and groups of moving objects in video surveillance systems. Most existing methods for the clustering focus on finding the optimum of overall partitioning. However, these approaches cannot provide meaningful descriptions of the clusters. Also, they are not very suitable for moving object databases since video data have spatial and temporal characteristics, and high-dimensional attributes. In this paper, we propose a model-based conceptual clustering (MCC) of moving objects in video surveillance based on a formal concept analysis. Our proposed MCC consists of three steps: 'model formation', 'model-based concept analysis', and 'concept graph generation'. The generated concept graph provides conceptual descriptions of moving objects. In order to assess the proposed approach, we conduct comprehensive experiments with artificial and real video surveillance data sets. The experimental results indicate that our MCC dominates two other methods, i.e., generality-based and error-based conceptual clustering algorithms, in terms of quality of concepts.
Lee, Jeongkyu; Rajauria, Pragya; Shah, Subodh K.
Learning cascaded shared-boost classifiers for part-based object detection.
This paper focuses on the problem of detecting a number of different class objects in images. We present a novel part-based model for object detection with cascaded classifiers. The coarse root and fine part classifiers are combined into the model. Different from the existing methods which learn root and part classifiers independently, we propose a shared-Boost algorithm to jointly train multiple classifiers. This paper is distinguished by two key contributions. The first is to introduce a new definition of shared features for similar pattern representation among multiple classifiers. Based on this, a shared-Boost algorithm which jointly learns multiple classifiers by reusing the shared feature information is proposed. The second contribution is a method for constructing a discriminatively trained part-based model, which fuses the outputs of cascaded shared-Boost classifiers as high-level features. The proposed shared-Boost-based part model is applied for both rigid and deformable object detection experiments. Compared with the state-of-the-art method, the proposed model can achieve higher or comparable performance. In particular, it can lift up the detection rates in low-resolution images. Also the proposed procedure provides a systematic framework for information reusing among multiple classifiers for part-based object detection. PMID:24808352
Yali Li; Shengjin Wang; Qi Tian; Xiaoqing Ding
DEFF Research Database (Denmark)
Functional imaging studies have demonstrated that processing of man-made objects activate the left ventral premotor cortex, which is known to be concerned with motor function. This has led to the suggestion that the comprehension of man-made objects may rely on motor-based knowledge of object utilization (action knowledge). Here we show that the left ventral premotor cortex is activated during categorization of "both" fruit/vegetables and articles of clothing, relative to animals and nonmanipulable man-made objects. This observation suggests that action knowledge may not be important for the processing of man-made objects per se, but rather for the processing of manipulable objects in general, whether natural or man-made. These findings both support psycholinguistic theories suggesting that certain lexical categories may evolve from, and the act of categorization rely upon, motor-based knowledge of action equivalency, and have important implications for theories of category specificity. Thus, the finding thatthe processing of vegetables/fruit and articles of clothing give rise to similar activation is difficult to account for should knowledge representations in the brain be truly categorically organized. Instead, the data are compatible with the suggestion that categories differ in the weight they put on different types of knowledge.
Gerlach, Christian; Law, Ian
The space environment is becoming more and more severe and crowded because of the rapid growth of space objects, which reveals an urgent demand to protect active satellites and other space assets. To accomplish such missions, e.g. the collision warning, the identification of space objects is important. In this paper, a three-stage approach for autonomous space object identification based on optical images is proposed. Firstly, on the basis of the approximate perspective imaging model, a scale and illumination invariant descriptor, composed of the normalized affine moment invariants (AMI) and the illumination invariant multiscale autoconvolution (MSA) transform, is developed to characterize the space object. Secondly, a multi-view modeling method is applied to construct multi-view databases of space objects for handling the viewpoint change. Finally, considering the extensibility of the databases, a K-nearest neighbor classifier is employed, and a K-means clustering is adopted to boost the search speed. Furthermore, to test the performance, a novel system based on the proposed approach is built and evaluated. The experimental evidence suggests that the system is stable and works well when the scale of a space object, the phase angle and the viewpoint change.
Ding, Hao; Li, Xudong; Zhao, Huijie
Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback
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Full Text Available An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword and their relations in a human-centered fashion. When querying for a specific semantic object (or objects, the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach.
Kompatsiaris Ioannis
Design and Evaluation of Perceptual-based Object Group Selection Techniques
Selecting groups of objects is a frequent task in graphical user interfaces. It is required prior to many standard operations such as deletion, movement, or modification. Conventional selection techniques are lasso, rectangle selection, and the selection and de-selection of items through the use of modifier keys. These techniques may become time-consuming and error-prone when target objects are densely distributed or when the distances between target objects are large. Perceptual-based selection techniques can considerably improve selection tasks when targets have a perceptual structure, for example when arranged along a line. Current methods to detect such groups use ad hoc grouping algorithms that are not based on results from perception science. Moreover, these techniques do not allow selecting groups with arbitrary arrangements or permit modifying a selection. This dissertation presents two domain-independent perceptual-based systems that address these issues. Based on established group detection models from perception research, the proposed systems detect perceptual groups formed by the Gestalt principles of good continuation and proximity. The new systems provide gesture-based or click-based interaction techniques for selecting groups with curvilinear or arbitrary structures as well as clusters. Moreover, the gesture-based system is adapted for the graph domain to facilitate path selection. This dissertation includes several user studies that show the proposed systems outperform conventional selection techniques when targets form salient perceptual groups and are still competitive when targets are semi-structured.
Dehmeshki, Hoda
The timing of feature-based attentional effects during object perception.
Allocating attention to basic features such as colour enhances perception of the respective features throughout the visual field. We have previously shown that feature-based attention also plays a role for more complex features required for object perception. To investigate at which level object perception is modulated by feature-based attention we recorded high-density event-related potentials (ERPs). Participants detected contour-defined objects or motion, and were informed to expect each feature dimension. Participants perceived contour-defined objects and motion better when they expected the congruent feature. This is consistent with modulation of the P1 when attending to lower-level features. For contours, modulation occurred at 290 ms, first at frontal electrodes and then at posterior sites, associated with sources in ventral visual areas accompanied by greater signal strength. This pattern of results is consistent with what has been observed in response to illusory contours. Our data provide novel insights into the contribution of feature-based attention to object perception that are associated with higher tier brain areas. PMID:21889519
Stojanoski, Boge; Niemeier, Matthias
Belief Network based Disambiguation of Object Reference in Spoken Dialogue System
This paper discusses a problem of human-machine interaction when spoken word to object reference ambiguity occurs. We study joint activity of several agents in which a remote robot finds an object while communicating with the user over a voice-only channel. We focus on the problem in which the robot disambiguates the reference of the uttered word or phrase to the target object. For example, the utterance of the word ``cup'' may refer to a ``teacup'', a ``coffee cup'', or even a ``glass'' for different users in some situations. This reference (hereafter, ``object reference'') is user and situation dependent. We conducted two experiments. The first experiment including 12 subjects confirmed that the user model of object references is significant. In the second experiment conducted on 20 subjects, we show the model reference sensitivity to the situation. In addition to the ambiguity of the object reference, the actual system must cope with two sources of uncertainty: speech and image recognition. We present the belief network based probabilistic reasoning system to determine the object reference. The resulting system demonstrates that the number of interactions needed to find a common reference is reduced as the user model is refined.
Yamakata, Yoko; Kawahara, Tatsuya; Okuno, Hiroshi G.; Minoh, Michihiko
Experimental video database management system based on advanced object-oriented techniques
Video data management is fast becoming one of the most important topics in multimedia databases. Most of the recent work on video databases has so far focused on video classification, feature extraction, spatial reasoning and image retrieval (video access); little work has been done on supporting advanced video editing and production activities, nor has there been much work done on providing facilities for efficient and versatile video data management. In this paper, we describe the development of an experimental video database system being implemented at HKUST, which employs extended object-oriented features and techniques. By incorporating conceptual object clustering concepts and techniques, it enables users to dynamically form, among other things, video programs (or segments) from existing objects based on semantic features/index terms. A prototype of this system has been constructed, using a persistent object storage manager (viz. EOS), on Sun4 workstations.
Huang, Liusheng; Lee, John C.; Li, Qing; Xiong, Wei
Feature Selection Method For Single Target Tracking Based On Object Interaction Models
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Full Text Available For single-target tracking problem Kernel-based method has been proved to be effective. A tracker which takes advantage of contextual information to incorporate general constraints on the shape and motion of objects will usually perform better when compare to the one that does not exploit this information. This is due to the reason that a tracker designed to give the best average performance in a variety of scenarios can be less accurate for a particular scene than a tracker that is attuned (by exploiting context to the characteristics of that scene. The use of a particular feature set for tracking will also greatly affect the performance. Generally, the features that best discriminate between multiple objects and, between the object and background are also best for tracking the object.
D.Vishnu Vardhan
Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling
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Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.
Qi Zhang
Cascade Boosting-Based Object Detection from High-Level Description to Hardware Implementation
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Full Text Available Object detection forms the first step of a larger setup for a wide variety of computer vision applications. The focus of this paper is the implementation of a real-time embedded object detection system while relying on high-level description language such as SystemC. Boosting-based object detection algorithms are considered as the fastest accurate object detection algorithms today. However, the implementation of a real time solution for such algorithms is still a challenge. A new parallel implementation, which exploits the parallelism and the pipelining in these algorithms, is proposed. We show that using a SystemC description model paired with a mainstream automatic synthesis tool can lead to an efficient embedded implementation. We also display some of the tradeoffs and considerations, for this implementation to be effective. This implementation proves capable of achieving 42 fps for 320×240 images as well as bringing regularity in time consuming.
K. Khattab
Cascade Boosting-Based Object Detection from High-Level Description to Hardware Implementation
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Full Text Available Object detection forms the first step of a larger setup for a wide variety of computer vision applications. The focus of this paper is the implementation of a real-time embedded object detection system while relying on high-level description language such as SystemC. Boosting-based object detection algorithms are considered as the fastest accurate object detection algorithms today. However, the implementation of a real time solution for such algorithms is still a challenge. A new parallel implementation, which exploits the parallelism and the pipelining in these algorithms, is proposed. We show that using a SystemC description model paired with a mainstream automatic synthesis tool can lead to an efficient embedded implementation. We also display some of the tradeoffs and considerations, for this implementation to be effective. This implementation proves capable of achieving 42 fps for images as well as bringing regularity in time consuming.
Khattab K
Efficient Fast Object-Tracking Scheme Based on Motion-vector-located Pattern Match
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Full Text Available In the process of object tracking, the major problem is how to mark the tracking box of the object. Moreover, multi-objects tracking is also difficult. This paper proposed and efficient fast object-tracking scheme based on motion-vector-located pattern match, which adopts motion vector of Mpeg2 to mark the moving targets in static video in order to mark and locate the targets automatically and quickly. Then, extract multi-dimensional characteristics from the initial targets taken by motion vector and make the model. Then accurately identifies the particles of larger weight and combines with inertia factor of velocity through matching the original data and the observations of particle filter. The matching of the characteristics of the new particle and the original one is more accurate and faster because of adopting the method of pattern classifying. The experiments show that the algorithm had good tracking performance and strong robustness.
Liubai Li
Directory of Open Access Journals (Sweden)
Full Text Available Since the examination paper generated with computer by the algorithms of random and backtracking takes on inferior quality and inefficient, and the question of generating examination paper with computer has the character of multi-ob-jective because of the index system metrics, the genetic algorithm with multi-objective strategy optimization is proposed to solve this problem. Mapping the index system to multi-objective functions and optimizing the computing with multi-objective strategy are employed in the algorithm. The genetic algorithm experiment based on the multi-objective strategy optimization shows that the result has the advantages getting tradeoff between performance and quality, and having the ability to tune the performance and quality to meet the user's requirements.
Yan Li
A New Merging Algorithm Based on Semantic Relationships of Learning Objects
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Full Text Available Learning Objects are key elements within e-Learning environment because describe the created educational material for students, besides, it permits the reusing and sharing in di_erent Learning Management Systems. Usually, when teachers need to create and structure educational experiences, they attend to repositories for retrieving resources ?tted to their interest, for reducing the e_ort and the computational time. In this paper, a proposal is presented for merging Learning Objects from heterogeneous repositories; the model is based on semantic relationships between Learning Objects retrieved from a meta-search engine, as an alternative for locating ?tted educational resources for teacher's interest. The model exposed in the proposal has been implemented as initial prototype, which retrieves Learning Objects from open repositories. An initial study results con?rm the usefulness of the model.
Elio Rivas-Sanchez
A prototype distributed object-oriented architecture for image-based automatic laser alignment
International Nuclear Information System (INIS)
Designing a computer control system for the National Ignition Facility (NIF) is a complex undertaking because of the system's large size and its distributed nature. The controls team is addressing that complexity by adopting the object-oriented programming paradigm, designing reusable software frameworks, and using the Common Object Request Broker Architecture (CORBA) for distribution. A prototype system for image-based automatic laser alignment has been developed to evaluate and gain experience with CORBA and OOP in a small distributed system. The prototype is also important in evaluating alignment concepts, image processing techniques, speed and accuracy of automatic alignment objectives for the NIF, and control hardware for aligment devices. The prototype system has met its inital objectives and provides a basis for continued development
Personalised learning object based on multi-agent model and learners' learning styles
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Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.
Noppamas Pukkhem
International Nuclear Information System (INIS)
Effect of Human factors on system safety is increasingly serious, which is often ignored in traditional probabilistic safety assessment methods however. A new probabilistic safety assessment model based on object-oriented Bayesian networks is proposed in this paper. Human factors are integrated into the existed event sequence diagrams. Then the classes of the object-oriented Bayesian networks are constructed which are converted to latent Bayesian networks for inference. Finally, the inference results are integrated into event sequence diagrams for probabilistic safety assessment. The new method is applied to the accident of loss of coolant in a nuclear power plant. the results show that the model is not only applicable to real-time situation assessment, but also applicable to situation assessment based certain amount of information. The modeling complexity is kept down and the new method is appropriate to large complex systems due to the thoughts of object-oriented. (authors)
Model-based approach for detection of objects in low-resolution passive-millimeter images
We describe a model-based vision system to assist the pilots in landing maneuvers under restricted visibility conditions. The system has been designed to analyze image sequences obtained from a passive millimeter wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere, but their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. We exploit these data to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. We also derive analytical expressions for the accuracy of the camera position estimate obtained by detecting the position of known objects in the image.
Tang, Yuan-Ling; Devadiga, Sadashiva; Kasturi, Rangachar; Harris, Randall L., Sr.
aDORe: a modular, standards-based Digital Object Repository
This paper describes the aDORe repository architecture, designed and implemented for ingesting, storing, and accessing a vast collection of Digital Objects at the Research Library of the Los Alamos National Laboratory. The aDORe architecture is highly modular and standards-based. In the architecture, the MPEG-21 Digital Item Declaration Language is used as the XML-based format to represent Digital Objects that can consist of multiple datastreams as Open Archival Information System Archival Information Packages (OAIS AIPs).Through an ingestion process, these OAIS AIPs are stored in a multitude of autonomous repositories. A Repository Index keeps track of the creation and location of all the autonomous repositories, whereas an Identifier Locator registers in which autonomous repository a given Digital Object or OAIS AIP resides. A front-end to the complete environment, the OAI-PMH Federator, is introduced for requesting OAIS Dissemination Information Packages (OAIS DIPs). These OAIS DIPs can be the stored OAIS ...
Van de Sompel, Herbert; Liu, X; Balakireva, L; Schwander, T; Sompel, Herbert Van de; Bekaert, Jeroen; Liu, Xiaoming; Balakireva, Luda; Schwander, Thorsten
An object-based conceptual model of a nuclear physics experiments database
International Nuclear Information System (INIS)
Nuclear physics experiments to be conducted at future accelerators will result in the accumulation of vast quantities of data. This paper briefly discusses an object-oriented database (OODB) approach for effectively managing this data and, more importantly, presents an object-based conceptual model of a nuclear physics experiments database as an initial by-product of applying this approach. The database model is provided by an object-relationship diagram (ORD). The concepts and conventions related to an ORD are explained, and the ORDs and associated terminology of the proposed model are given. Two prototypes that are currently being developed based on the model are also briefly discussed. The nuclear physics experiments database model and prototypes result from software research and development efforts associated with the Continuous Electron Beam Accelerator Facility (CEBAF). (orig.)
Automatic object detection in point clouds based on knowledge guided algorithms
The modeling of real-world scenarios through capturing 3D digital data has been proven applicable in a variety of industrial applications, ranging from security, to robotics and to fields in the medical sciences. These different scenarios, along with variable conditions, present a challenge in discovering flexible appropriate solutions. In this paper, we present a novel approach based on a human cognition model to guide processing. Our method turns traditional data-driven processing into a new strategy based on a semantic knowledge system. Robust and adaptive methods for object extraction and identification are modeled in a knowledge domain, which has been created by purely numerical strategies. The goal of the present work is to select and guide algorithms following adaptive and intelligent manners for detecting objects in point clouds. Results show that our approach succeeded in identifying the objects of interest while using various data types.
Truong, Hung; Karmacharya, Ashish; Mordwinzew, Waldemar; Boochs, Frank; Chudyk, Celeste; Habed, Adlane; Voisin, Yvon
Digital Repository Infrastructure Vision for European Research (DRIVER)
Environmental protection is now an important area of study as well as the global concern. Many teaching and learning may involve the environment as part of their study. In engineering, there are many topics related to them, but most of the syllabus may too theoretical and it is necessary to develop an object based method to assist students' learning. A demonstration of the environmental protection and energy saving concept to Outcome-based education through a web-site development has been s...
Cheng, Ka Wai E.; Weimin Wang
SATEN An Object-Oriented Web-Based Revision and Extraction Engine
SATEN is an object-oriented web-based extraction and belief revision engine. It runs on any computer via a Java 1.1 enabled browser such as Netscape 4. SATEN performs belief revision based on the AGM approach. The extraction and belief revision reasoning engines operate on a user specified ranking of information. One of the features of SATEN is that it can be used to integrate mutually inconsistent commensuate rankings into a consistent ranking.
Williams, M A; Williams, Mary-Anne; Sims, Aidan
The field of marriage and family therapy (MFT) has recently engaged in the process of defining core competencies for the profession. Many MFT training programs are adapting their curriculum to develop more competency-based training strategies. The Objective Structured Clinical "Examination" (OSCE) is widely used in the medical profession to assess…
Miller, John K.
Presents two methods for including weights in distance-based nonlinear multivariate data analysis. One method assigns weights to the objects, while the other is concerned with differential weighing of groups of variables. Discusses applications of these weighting schemes and proposed an algorithm to minimize the corresponding loss function. (SLD)
Commandeur, Jacques J. F.; Groenen, Patrick J. F.; Meulman, Jacqueline J.
As part of their undergraduate mathematics curriculum, students at Brock University learn to create and use computer-based tools with dynamic, visual interfaces, called Exploratory Objects, developed for the purpose of conducting pure or applied mathematical investigations. A student's Development Process Model of creating and using an…
Marshall, Neil; Buteau, Chantal
An Unsupervised Approach to Activity Recognition and Segmentation based on Object-Use Fingerprints
DEFF Research Database (Denmark)
Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use ?ngerprints to recognize activities without human labeling. We show how to build our activity models based on object-use ?ngerprints, which are sets of contrast patterns describing signi?cant differences of object use between any two activity classes. We then propose a ?ngerprint-based algorithm to recognize activities. We also propose two heuristic algorithms based on object relevance to segment a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our recognition algorithm achieves a precision of 91.4% and a recall 92.8%, and the segmentation algorithm achieves an accuracy of 93.1% on the dataset we collected.
Gu, Tao; Chen, Shaxun
Object-based analysis of hyperspectral and thermal infrared satellite imagery
International Nuclear Information System (INIS)
The given paper proposes an object-based procedure for the combined analysis of high-resolution optical, thermal infrared and hyperspectral satellite imagery for different nuclear safeguards-related tasks. Some case studies using Hyperion, Landsat, QuickBird and Ikonos data will demonstrate the advantages of this approach. (author)
Object-Based Analysis of Airborne LiDAR Data for Building Change Detection
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Full Text Available Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs, digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m2. The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.
Shiyan Pang
Comprehensive Analysis with KVM Techniques and Implementation Of Object Pool Based On J2ME RMS
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Full Text Available J2ME services play an important role in the field of Communication industry. In this paper, we discuss and analyze the consumptive behaviour based on object pool with RMS capabilities. We discuss and analyze different aspects of RMS mining techniques and their behaviour in mobile devices. We also analyze the better method or rule of implementing services which is more suitable for mobile devices. The method this paper mentioned has benefit to analyze large numbers of data in consumptive behaviours and provides some instructions to improve better marketing in concerned fields. In this paper we use J2ME components like CLDC (Connected Limited Device Configuration and MIDP (Mobile Information Device Profile with data mining services (DMS that provide local storage, a user interface, and networking capabilities that runs on mobile computing devices. We also discuss the need of Object Pool in mobile devices to enhance the capability of mobile devices. Object pool model based on RMS is proposed. Aimed to solve the Memory peak problem in J2ME, on the basis of object pool design pattern, an object pool model used RMS is designed and implemented.
Ms. Nandika Sood
Web Services Based Learning Objects Generator for Device-Independent M-Learning
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Full Text Available Learning objects, which are the base component of m-learning system, are usually target to modifications in contexts and formats. The device- dependent applications of hand-held devices have proven to be ineffective for creating m-learning courseware. Learning Objects Metadata (LOM is the most popular standard specification for learning objects but lacks the ability to facilitate platforms descriptions. This paper outlines various aspects of design and implementation of Web Services Oriented Rendering Architecture (WSORA which combines LOM Editor with any available published web services. This arrangement is devised in order to make a device-independent m-learning gateway between different mobile devices, such as cell phones, PDA's, palmtops, and laptops and the vast learning objects available on the World Wide Web. The key technologies behind WSORA are extending the IEEE LOM base scheme structure, LOM Editor, device-independent LO generator, and web services. The major advantage of WSORA is thus achieved to give mobile devices of different types clean and quick access to learning objects customarily designed for desktop browsers.
Akram Moh. Alkouz
Autonomic Management of Object Replication for FT-CORBA Based Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) comprises the electronics, communications or information processing used singly or integrated to improve the efficiency or safety of surface transportation. Accordingly, the ITS has to perform collection, management, and provision of real time transport information reliably. It can be deployed based on the Common Object Request Broker Architecture (CORBA) of the Object Management Group (OMG) because it consists of many interconnected heterogeneous systems deployed by independent organizations. Fault Tolerant CORBA (FT-CORBA) supports real time requirement of transport information stably through redundancy by replication of server objects. However, object replication, management, and related protocols of FT-CORBA require extra system resources of CPU and memory, and can degrade the system performance both locally and as a whole. This paper proposes an architecture to enhance performance of FT-CORBA based ITS in terms of CPU and memory by managing object replication adaptively during system operation with an agent. The application of the agent is expected to support fault tolerance of real ITS efficiently.
Suh, Woonsuk; Lee, Eunseok
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Full Text Available Classification of objects in video stream is important because of its application in many emerging areas such as visual surveillance, content based video retrieval and indexing etc. The task is far more challenging because the video data is of heavy and highly variable nature. The processing of video data is required to be in real-time. This paper presents a multiclass object classification technique using machine learning approach. Haar-like features are used for training the classifier. The feature calculation is performed using Integral Image representation and we train the classifier offline using a Stage-wise Additive Modeling using a Multiclass Exponential loss function (SAMME. The validity of the method has been verified from the implementation of a real-time human-car detector. Experimental results show that the proposed method can accurately classify objects, in video, into their respective classes. The proposed object classifier works well in outdoor environment in presence of moderate lighting conditions and variable scene background. The proposed technique is compared, with other object classification techniques, based on various performance parameters.
Chandra Mani Sharma
A Generalized Definition Language for Implementing the Object Based Fuzzy Class Model
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Full Text Available The emerging application domains in Engineering, Scientific Technology, Multimedia, GIS, Knowledge management, Expert system design etc require advanced data models to represent and manipulate the data values, because the information resides in these domains are often vague or imprecise in nature & difficult to represent while implementing the application software. In order to fulfill the requirements of such application demands, researchers have put the innovative concept of object based fuzzy database system by extending the object oriented system and adding fuzzy techniques to handle complex object and imprecise data together. Some extensions of the OODMS have been proposed in the literature, but what is still lacking a unifying & systematic formalization of these dedicated concepts. This paper is the consequence research of our previous work, in which we proposed an effective & formal Fuzzy class model to represent all type of fuzzy attributes & objects those can be confined to fuzzy class. Here, we introduce a generalized definition language for the fuzzy class which can efficiently define the proposed fuzzy class model along with all possible fuzzy data type to describe the structure of the database & thus serve as data definition language for the object based fuzzy database system.
Debasis Dwibedy, Dr. Laxman Sahoo, Sujoy Dutta
Directory of Open Access Journals (Sweden)
Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.
Lei Qin
Directory of Open Access Journals (Sweden)
Full Text Available The problem of unsupervised and supervised learning of RBF networks is discussed with Multi-Objective Particle Swarm Optimization (MOPSO. This study presents an evolutionary multi-objective selection method of RBF networks structure. The candidates of RBF networks structures are encoded into particles in PSO. These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. This study suggests an approach of RBF network training through simultaneous optimization of architectures and connections with PSO-based multi-objective algorithm. Present goal is to determine whether MOPSO can train RBF networks and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that; the best results are obtained for our proposed method that has obtained 100 and 80.21% classification accuracy from the experiments made on the data taken from breast cancer and diabetes diseases database, respectively. The results also show that our approach provides an effective means to solve multi-objective RBF networks and outperforms multi-objective genetic algorithm.
S.N. Qasem
Object Search for the Internet of Things Using Tag-based Location Signatures
Directory of Open Access Journals (Sweden)
Full Text Available In this paper, an object search solution for the Internet of Things (IoT is proposed. This study first differentiates localization and searching. Localization is to calculate an object's current location. Searching is to return a set of locations where a target object could be. It is possible that the locations of the returned set are not contiguous. Searching accuracy can be improved if the number of the returned locations is small. Even though localization technique is applicable to searching applications, a simpler and easier solution will attract more enterprise users. In this paper, based on a concept called location signature, defined by a set of reference tags, an object searching method named Location Signature Search (LSS is proposed. The study of LSS shows that the searching accuracy can be very high if a location signature is not shared by too many locations. Since location signatures are affected by the deployment of the reference tags, trade-off between searching accuracy and implementation cost is achievable. A real world experiment is conducted in this research. The results show that LSS indeed is a practical method for object searching applications.
Jung-Sing Jwo
Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis
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Full Text Available The devastating series of fire events that occurred during the summers of 2007 and 2009 in Greece made evident the need for an operational mechanism to map burned areas in an accurate and timely fashion to be developed. In this work, Systčme pour l'Observation de la Terre (SPOT-4 HRVIR images are introduced in an object-based classification environment in order to develop a classification procedure for burned area mapping. The development of the procedure was based on two images and then tested for its transferability to other burned areas. Results from the SPOT-4 HRVIR burned area mapping showed very high classification accuracies ( 0.86 kappa coefficient, while the object-based classification procedure that was developed proved to be transferable when applied to other study areas.
Anastasia Polychronaki
Artificial Bee Colony Algorithm based Multi-Objective Node Placement for Wireless Sensor Network
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Full Text Available The recent popularity of applications based on wireless sensor networks (WSN provides a strong motivation for pursuing research in different dimensions of WSN. Node placement is an essential task in wireless sensor network and is a multi-objective combinatorial problem in nature. The positions of sensor nodes are very important and must be able to provide maximum coverage with longer lifetimes. So, for efficient node placement, a novel multi-objective Artificial Bee Colony (ABC algorithm based framework is proposed in this paper. The framework optimizes the operational modes of the sensor nodes along with clustering schemes and transmission signal strengths. The results show that the proposed algorithm outperformed the contemporary methodology based on TPSMA, PSO and ACO.
R K Jena
Query and Update Efficient B+-Tree Based Indexing of Moving Objects
DEFF Research Database (Denmark)
A number of emerging applications of data management technology involve the monitoring and querying of large quantities of continuous variables, e.g., the positions of mobile service users, termed moving objects. In such applications, large quantities of state samples obtained via sensors are streamed to a database. Indexes for moving objects must support queries efficiently, but must also support frequent updates. Indexes based on minimum bounding regions (MBRs) such as the R-tree exhibit high concurrency overheads during node splitting, and each individual update is known to be quite costly. This motivates the design of a solution that enables the B+-tree to manage moving objects. We represent moving-object locations as vectors that are timestamped based on their update time. By applying a novel linearization technique to these values, it is possible to index the resulting values using a single B+-tree that partitions values according to their timestamp and otherwise preserves spatial proximity. We develop algorithms for range and k nearest neighbor queries, as well as continuous queries. The proposal can be grafted into existing database systems cost effectively. An extensive experimental study explores the performance characteristics of the proposal and also shows that it is capable of substantially outperforming the R-tree based TPR-tree for both single and concurrent access scenarios.
Jensen, Christian SŘndergaard; Lin, Dan
Lossy to lossless object-based coding of 3-D MRI data.
We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature. PMID:18249726
Menegaz, Gloria; Thiran, Jean-Philippe
Implementation of intelligent nuclear material diagnosis module based on the component object model
International Nuclear Information System (INIS)
In this paper, the implementation techniques of intelligent nuclear material surveillance system based on the COM (Component Object Model) and SOM (Self Organized Mapping) was described. The surveillance system that is to be developed is consist of CCD cameras, neutron monitors, and PC for data acquisition. To develop the system, the properties of the COM based software development technology was investigated, and the characteristics of related platform APIs was summarized. This report could be used for the developers who want to develop the intelligent surveillance system for various experimental environments based on the DVR and sensors using Borland C++ Builder
Wnbac: A Weighted Network Based Adaptive Clustering Algorithm for Spatial Objects
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Full Text Available To overcome shortcomings such as slowness of the convergence, sensitive to initial value and pre-awareness of dataset in most clustering algorithm, a Weighted Network Based Adaptive Clustering (WNBAC algorithm is put forward. The WNBAC algorithm is to build the weighted network for spatial objects in term of the similarity among objects, then to partition nodes in the weighted network by nodes' strength and edges' weight. The core idea, main process, building procedure and parameter setting for the WNBAC algorithm are described and discussed in details. Experiment results indicate that the proposed WNBAC algorithm is both effective and efficient.
Pan Xu-Wei
Affine Invariant, Model-Based Object Recognition Using Robust Metrics and Bayesian Statistics
We revisit the problem of model-based object recognition for intensity images and attempt to address some of the shortcomings of existing Bayesian methods, such as unsuitable priors and the treatment of residuals with a non-robust error norm. We do so by using a refor- mulation of the Huber metric and carefully chosen prior distributions. Our proposed method is invariant to 2-dimensional affine transforma- tions and, because it is relatively easy to train and use, it is suited for general object matching problems.
Zografos, Vasileios; 10.1007/11559573_51
Fractal-based texture segmentation and artifact object separation in natural scene images
This paper presents a fractal-based method for natural scene image segmentation. The main goal is to find artifact objects from complex natural scene. We propose a set of fractal measurements in order to acquire various aspects of roughness of each part of an image. The performance of the data fitting in the box-dimension estimation is analyzed and an improved algorithm is proposed. Experiments prove that the proposed approach is suitable for texture segmentation and artifact object finding in natural environment images.
Yang, Bo; Xu, Guang-you; Zhu, Zhigang
Modelica-based object-orient modeling of rotor system with multi-faults
Modelica-based object-orient method is proved to be rapid, accurate and easy to modify, which is suitable for prototype modeling and simulation of rotor system, whose parameters need to be modified frequently. Classical non-object-orient method appears to be inefficient because the code is difficult to modify and reuse. An adequate library for object-orient modeling of rotor system with multi-faults is established, a comparison with non-object-orient method on Jeffcott rotor system and a case study on turbo expander with multi-faults are implemented. The relative tolerance between object-orient method and non-object-orient is less than 0.03%, which proves that these two methods are as accurate as each other. Object-orient modeling and simulation is implemented on turbo expander with crack, rub-impact, pedestal looseness and multi-faults simultaneously. It can be conclude from the case study that when acting on compress side of turbo expander separately, expand wheel is not influenced greatly by crack fault, the existence of rub-impact fault forces expand wheel into quasi-periodic motion and the orbit of expand wheel is deformed and enhanced almost 1.5 times due to pedestal looseness. When acting simultaneously, multi-faults cannot be totally decomposed but can be diagnosed from the feature of vibration. Object-orient method can enhance the efficiency of modeling and simulation of rotor system with multi-faults, which provides an efficient method on prototype modeling and simulation.
Li, Ming; Wang, Yu; Li, Fucai; Li, Hongguang; Meng, Guang
An object-based approach to hierarchical classification of the Earth's topography from SRTM data
Digital classification of the Earth's surface has significantly benefited from the availability of global DEMs and recent advances in image processing techniques. Such an innovative approach is object-based analysis, which integrates multi-scale segmentation and rule-based classification. Since the classification is based on spatially configured objects and no longer on solely thematically defined cells, the resulting landforms or landform types are represented in a more realistic way. However, up to now, the object-based approach has not been adopted for broad-scale topographic modelling. Existing global to almost-global terrain classification systems have been implemented on per cell schemes, accepting disadvantages such as the speckled character of outputs and the non-consideration of space. We introduce the first object-based method to automatically classify the Earth's surface as represented by the SRTM into a three-level hierarchy of topographic regions. The new method relies on the concept of decomposing land-surface complexity into ever more homogeneous domains. The SRTM elevation layer is automatically segmented and classified at three levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these recognised scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of the classes satisfy the regionalisation requirements of maximising internal homogeneity while minimising external homogeneity. Most objects have boundaries matching natural discontinuities at the regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as an eCognition® customised process, available as free online download. The results are embedded in a web application, where users can visualise and download the data of interest in GIS ready vector format. The method has originally been developed on the SRTM, but may be applied to any other DEM and regional area of interest. The tool allows for modifications in order to meet the requirements of individual research tasks. Both segmentation and class thresholds are relative to the extent and characteristics of the input DEM. Therefore, when applying the tool to regional or national scales, the results should be interpreted within the adequate context.
Eisank, C.; Dragut, L.
Static Object Detection Based on a Dual Background Model and a Finite-State Machine
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Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.
Heras Evangelio Rubén
Man-made Object Detection Based on Texture Clustering and Geometric Structure Feature Extracting
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Full Text Available Automatic aerial image interpretation is one of new rising high-tech application fields, and it's proverbially applied in the military domain. Based on human visual attention mechanism and texture visual perception, this paper proposes a new approach for man-made object detection and marking by extracting texture and geometry structure features. After clustering the texture feature to realize effective image segmentation, geometry structure feature is obtained to achieve final detection and marking. Thus a man-made object detection methodology is designed, by which typical man-made objects in complex natural background, including airplanes, tanks and vehicles can be detected. The experiments sustain that the proposed method is effective and rational.
Fei Cai
Kernel Based Approach toward Automatic object Detection and Tracking in Surveillance Systems
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Full Text Available A modified object-tracking algorithm that uses the flexible Metric Distance Transform kernel and multiple features for the Mean shift procedure is proposed and tested. The Faithful target separation based on RGB joint pdf of the target region and that of a neighborhood surrounding the object is obtained. The non-linear log-likelihood function maps the multimodal object/background distribution as positive values for colors associated with foreground, while negative values are marked for background. This replaces the more usual Epanechnikov kernel (E-kernel, improving target representation and localization without increasing the processing time, minimizing the similarity measure using the Bhattacharya coefficient. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.
Amir Aliabadian
Multi-objective Structural Optimization Base on Improved NSGA-II Algorithm
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Full Text Available A kind of fast and elitist multi-objective genetic algorithm (nondominated sorting genetic algorithm -II was presented to solve high dimension and multi-modal optimal problems. T His fuzzy information could be converted into a mathematically well-structured problem based on fuzzy optimal theory. And the improved crossover operator of NSGA-II was applied to obtain the optimal solution. According to the test results on a typical test function and an application on the structural fuzzy multi-objective optimization of three-bar truss, more reasonable distributed solutions could be obtained and the diversity of the solutions could be maintained. It provides beneficial references for engineering application of fuzzy multi-objective structure optimization.
M.Z. Lai
Reflection-mode optical-resolution photoacoustic microscopy based on a reflective objective.
We developed a new reflection-mode optical-resolution photoacoustic microscopy (OR-PAM) based on the cooperation of a reflective objective and an ultrasonic transducer. The reflective objective is used to produce nearly diffraction-limited optical focusing, and the excited ultrasound waves are then directly detected by an ultrasonic transducer that was placed in the central cone of the objective. This new design avoids the coupling between optical focusing and ultrasound transmission in the reflection mode. Moreover, the proposed system is able to provide lateral resolution of 1.2 ?m at 580 nm, penetration depth of 0.9 mm in biological tissues, and a work distance of 6.0 mm. We present in vivo imaging of the microvasculature in mouse ears and in vitro imaging of red blood cells (RBCs), which demonstrate the capability of the system to study microcirculation. PMID:24104331
Wang, Hui; Yang, Xiaoquan; Liu, Yanyan; Jiang, Bowen; Luo, Qingming
Typical multidisciplinary design optimization(MDO) has gradually been proposed to balance performances of lightweight, noise, vibration and harshness(NVH) and safety for instrument panel(IP) structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene(PP) under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment(DOE), response surface model(RSM), and collaborative optimization(CO) are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm(MOGA), the optimal Pareto sets are completed to solve the multi-objective optimization(MOO) problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.
Wang, Ping; Wu, Guangqiang
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Full Text Available An important question in information retrieval is how to create a database index which can be searched efficiently for the data one seeks. One such technique called signature file based access method is preferred for its easy handling of insertion and update operations. Most of the proposed methods use either efficient search method or tree based intermediate data structure to filter data objects matching the query. Use of search techniques retrieves the objects by sequentially comparing the positions of 1s in it. Such methods take longer retrieval time. On the other hand tree based structures traverse multiple paths making comparison process tedious. This study describes a new indexing technique for object-oriented data bases using the dynamic balancing of B+ tree called SD (Signature Declustering tree. The SD-tree represents all 1s in signatures in a compact manner that results in saving of insertion and searching time. Analytical experiments have been conducted by varying the signature length and the distribution of signature weight. The study clearly indicates the advantage of fast retrieval time in a way quite different from the other methods suggested in the past.
I. Elizabeth Shanthi
Geometrically-Correct Projection-Based Texture Mapping onto a Deformable Object.
Projection-based Augmented Reality commonly employs a rigid substrate as the projection surface and does not support scenarios where the substrate can be reshaped. This investigation presents a projection-based AR system that supports deformable substrates that can be bent, twisted or folded. We demonstrate a new invisible marker embedded into a deformable substrate and an algorithm that identifies deformations to project geometrically correct textures onto the deformable object. The geometrically correct projection-based texture mapping onto a deformable marker is conducted using the measurement of the 3D shape through the detection of the retro-reflective marker on the surface. In order to achieve accurate texture mapping, we propose a marker pattern that can be partially recognized and can be registered to an object?s surface. The outcome of this work addresses a fundamental vision recognition challenge that allows the underlying material to change shape and be recognized by the system. Our evaluation demonstrated the system achieved geometrically correct projection under extreme deformation conditions. We envisage the techniques presented are useful for domains including prototype development, design, entertainment and information based AR systems. PMID:24650981
Fujimoto, Yuichiro; Smith, Ross T; Taketomi, Takafumi; Yamamoto, Goshiro; Miyazaki, Jun; Kato, Hirokazu; Thomas, Bruce H
Image Coding Scheme Based on Object Extraction and Hybrid Transformation Technique
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Full Text Available This paper describes an efficient object-based hybrid image coding (OB-HIC scheme. The proposed scheme is based on using the discrete wavelet transform (DWT in conjunction with the discrete cosine transform (DCT to provide coding performance superior to the popular image coders. The proposed method uses combination of the object-based DCT coding and the high performance of the set partitioning in hierarchical tree (SPIHT coding. The subband image data in the wavelet domain is modified based on the DCT and the object classification of the coefficient in the low-frequency image subband (LL. The modification process provides a new subband image data containing almost the same information of the original one but having smaller values of the wavelet coefficients. Simulation results of the proposed method demonstrate that, with small addition in the computational complexity of the coding process, the peak signal-to-noise ratio (PSNR performance of the proposed algorithm is much higher than that of the SPIHT test coder and some of famous image coding techniques.
Usama S. Mohammed
Transmission of object based fine-granular-scalability video over networks
It is a hot focus of current researches in video standards that how to transmit video streams over Internet and wireless networks. One of the key methods is FGS(Fine-Granular-Scalability), which can always adapt to the network bandwidth varying but with some sacrifice of coding efficiency, is supported by MPEG-4. Object-based video coding algorithm has been firstly included in MPEG-4 standard that can be applied in interactive video. However, the real time segmentation of VOP(video object plan) is difficult that limit the application of MPEG-4 standard in interactive video. H.264/AVC is the up-to-date video-coding standard, which enhance compression performance and provision a network-friendly video representation. In this paper, we proposed a new Object Based FGS(OBFGS) coding algorithm embedded in H.264/AVC that is different from that in mpeg-4. After the algorithms optimization for the H.264 encoder, the FGS first finish the base-layer coding. Then extract moving VOP using the base-layer information of motion vectors and DCT coefficients. Sparse motion vector field of p-frame composed of 4*4 blocks, 4*8 blocks and 8*4 blocks in base-layer is interpolated. The DCT coefficient of I-frame is calculated by using information of spatial intra-prediction. After forward projecting each p-frame vector to the immediate adjacent I-frame, the method extracts moving VOPs (video object plan) using a recursion 4*4 block classification process. Only the blocks that belong to the moving VOP in 4*4 block-level accuracy is coded to produce enhancement-layer stream. Experimental results show that our proposed system can obtain high interested VOP quality at the cost of fewer coding efficiency.
Shi, Xu-li; Jin, Zhi-cheng; Teng, Guo-wei; Zhang, Zhao-yang; An, Ping; Xiao, Guang
Research and development of infrared object detection system based on FPGA
Infrared object detection is an important technique of digital image processing. It is widely used in automatic navigation, intelligent video surveillance systems, traffic detection, medical image processing etc. Infrared object detection system requires large storage and high speed processing technology. The current development trend is the system which can be achieved by hardware in real-time with fewer operations and higher performance. As a main large-scale programmable specific integrated circuit, field programmable gate array (FPGA) can meet all the requirements of high speed image processing, with the characteristics of simple algorithm realization, easy programming, good portability and inheritability. So it could get better result by using FPGA to infrared object detection system. According to the requirements, the infrared object detection system is designed on FPGA. By analyzing some of the main algorithms of object detection, two new object detection algorithms called integral compare algorithm (ICA) and gradual approach centroid algorithm (GACA) are presented. The system design applying FPGA in hardware can implement high speed processing technology, which brings the advantage of both performance and flexibility. ICA is a new type of denoising algorithm with advantage of lower computation complexity and less execution time. What is more important is that this algorithm can be implemented in FPGA expediently. Base on image preprocessing of ICA, GACA brings high positioning precision with advantage of insensitivity to the initial value and fewer times of convergence iteration. The experiments indicate that the infrared object detection system can implement high speed infrared object detecting in real-time, with high antijamming ability and high precision. The progress of Verilog-HDL and its architecture are introduced in this paper. Considering the engineering application, this paper gives the particular design idea and the flow of this method's realization in FPGA device. And we also discuss the problems on how to describe the hardware system in Verilog-HDL. Based on the hardware architecture of infrared object detection system, the component units of the system are discussed in detail, such as image data acquisition unit, data pre-processing unit and logical control unit etc. The design of the FPGA function and its implementation are carried on Verilog-HDL with TOP-DOWN method. The ending is the prospect of the project.
Zhao, Jianhui; He, Jianwei; Wang, Pengpeng; Li, Fan
Design of objective lenses to extend the depth of field based on wavefront coding
Wavefront coding extended the depth of field to a great extent with simpler structure compared to confocal microscope. With cubic phase mask (CPM) employed in the STOP of the objective lens, blurred images will be obtained in charge coupled device (CCD), which will be restored to sharp images by Wiener filter. We proposed that one CPM is used in one microscope although there are different objective lenses with different power indices. The microscope proposed here is the wavefront coding one when the CPM is used in the STOP; while it is the traditional one when a plane plate is used in the STOP. Firstly, make the STOP in the last surface of the lens, and then add a plane plate at the STOP with the same material and the same center thickness of the CPM. Traditional objective lenses are designed, based on which wavefront coding system will be designed with the plane plate replaced by a CPM. Secondly, the parameters of CPMs in different objective lenses are optimized to certain ranges based on metric function of stability of modulation transfer function (MTF). The optimal parameter is chosen from these ranges. A set of objective lenses is designed as an example with one CPM. The simulation results shows that the depth of field of 4X, 10X, 40X, 60X and 100X objective lenses with the same CPM can reach to 400um, 40um, 24um, 16um and 2um respectively, which is much larger than 55.5um, 8.5um, 1um, 0.4um and 0.19um of the traditional ones.
Zhao, Tingyu; Ye, Zi; Zhang, Wenzi; Huang, Weiwei; Yu, Feihong
Data-driven hierarchical structure kernel for multiscale part-based object recognition.
Detecting generic object categories in images and videos are a fundamental issue in computer vision. However, it faces the challenges from inter and intraclass diversity, as well as distortions caused by viewpoints, poses, deformations, and so on. To solve object variations, this paper constructs a structure kernel and proposes a multiscale part-based model incorporating the discriminative power of kernels. The structure kernel would measure the resemblance of part-based objects in three aspects: 1) the global similarity term to measure the resemblance of the global visual appearance of relevant objects; 2) the part similarity term to measure the resemblance of the visual appearance of distinctive parts; and 3) the spatial similarity term to measure the resemblance of the spatial layout of parts. In essence, the deformation of parts in the structure kernel is penalized in a multiscale space with respect to horizontal displacement, vertical displacement, and scale difference. Part similarities are combined with different weights, which are optimized efficiently to maximize the intraclass similarities and minimize the interclass similarities by the normalized stochastic gradient ascent algorithm. In addition, the parameters of the structure kernel are learned during the training process with regard to the distribution of the data in a more discriminative way. With flexible part sizes on scale and displacement, it can be more robust to the intraclass variations, poses, and viewpoints. Theoretical analysis and experimental evaluations demonstrate that the proposed multiscale part-based representation model with structure kernel exhibits accurate and robust performance, and outperforms state-of-the-art object classification approaches. PMID:24808345
Botao Wang; Hongkai Xiong; Xiaoqian Jiang; Zheng, Yuan F
A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection
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Full Text Available We propose a 3-step algorithm for the automatic detection of moving objects in video sequences using region-based active contours. First, we introduce a very full general framework for region-based active contours with a new Eulerian method to compute the evolution equation of the active contour from a criterion including both region-based and boundary-based terms. This framework can be easily adapted to various applications, thanks to the introduction of functions named descriptors of the different regions. With this new Eulerian method based on shape optimization principles, we can easily take into account the case of descriptors depending upon features globally attached to the regions. Second, we propose a 3-step algorithm for detection of moving objects, with a static or a mobile camera, using region-based active contours. The basic idea is to hierarchically associate temporal and spatial information. The active contour evolves with successively three sets of descriptors: a temporal one, and then two spatial ones. The third spatial descriptor takes advantage of the segmentation of the image in intensity homogeneous regions. User interaction is reduced to the choice of a few parameters at the beginning of the process. Some experimental results are supplied.
Aubert Gilles
Simplified production of multimedia based radiological learning objects using the flash format
International Nuclear Information System (INIS)
Purpose: evaluation of the applicability of the flash format for the production of radiological learning objects used in an e-learning environment. Material and methods: five exemplary learning objects with different didactic purposes referring to radiological diagnostics are presented. They have been intended for the use within the multimedia, internet-based e-learning environment LaMedica. Interactive learning objects were composed using the Flash 5.0 software (Macromedia, San Francisco, USA) on the basis of digital CT and MR images, digitized conventional radiographs and different graphical elements prepared as TIFF files or in a vector graphics format. Results: after a short phase of initial skill adaptation training, a radiologist author was soon able to create independently all learning objects. The import of different types of images and graphical elements was carried out without complications. Despite manifold design options, handling of the program is easy due to clear arrangement and structure, thus enabling the creation of simple as well as complex learning objects that provided a high degree of attractiveness and interaction. Data volume and bandwidth demand for online use was significantly reduced by the flash format compression without a substantial loss of visual quality. (orig.)
A new method of synthesizing computer-generated hologram of three-dimensional (3D) objects is proposed from their projection images. A series of projection images of 3D objects are recorded with one-dimensional azimuth scanning. According to the principles of paraboloid of revolution in 3D Fourier space and 3D central slice theorem, spectra information of 3D objects can be gathered from their projection images. Considering quantization error of horizontal and vertical directions, the spectrum information from each projection image is efficiently extracted in double circle and four circles shape, to enhance the utilization of projection spectra. Then spectra information of 3D objects from all projection images is encoded into computer-generated hologram based on Fourier transform using conjugate-symmetric extension. The hologram includes 3D information of objects. Experimental results for numerical reconstruction of the CGH at different distance validate the proposed methods and show its good performance. Electro-holographic reconstruction can be realized by using an electronic addressing reflective liquid-crystal display (LCD) spatial light modulator. The CGH from the computer is loaded onto the LCD. By illuminating a reference light from a laser source to the LCD, the amplitude and phase information included in the CGH will be reconstructed due to the diffraction of the light modulated by the LCD.
Huang, Sujuan; Wang, Duocheng; He, Chao
A Preliminary Correctness Evaluation Model of Object-Oriented Software Based on UML
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Full Text Available Concurrent engineering is a philosophy that attempts to take into account of all the activities of a product life cycle early in the design stage. In the manufacturing industry, approximately 70% of a product`s manufacting and assembly costs are determined during the design stage. Similar to the software industry system analysis and design has significant influence on later activities of the software development life cycle. Object-oriented approach has been the main stream for software development, and unified Modeling Language (UML integrates most of the object-oriented modeling methods and has become the standards. This paper incorporates the CE concept into the evaluation of object-oriented software development and proposes a Hierarchical Aggregation Model (HAM to early evaluate the object-oriented software design quality based on UML. There are three advantages of using this model. First, this model can help reduce the project risk, cost, and time span, and eventually improve the software quality early in the software development life cycle. Secondly, this model facilitates the use of the standards of object-oriented modeling language, UML, which makes proposed model more applicable to real software development. Thirdly, this model is easy to implement, that can potentially be imbedded in CASE tools to directly support the project manager`s decision making.
S. Wesley Changchien
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Full Text Available Video tracking is the process of locating a moving object in time that is visualized by camera and are widely used in surveillance, animation and robotics Tracking describes the process of recording movement and translating that movement onto a digital model. The set of constraints that produce the most accurate tracking is the one that describes better the action performed. The key difficulty in video tracking is to associate target locations in consecutive video frames, especially when the objects are moving fast relative to the frame rate. Here, a video tracking system is been employed in which motion model describes how the image of the target might change for different possible motions of the object to track. The role of the tracking algorithm adopted for this system is to analyze the video frames to estimate the motion parameters. These parameters characterize the location of the target. In this research, three features are been extracted from each moving objects such as centroid, area, average luminance. Finally the similarity function is applied to tracking and the attempt proves that the chosen method has good performance under dynamic circumstances for real time tracking. Simulink is integrated with MATLAB to build a model for object tracking and data transfer is easily handled between the programs. The Simulink based customizable framework is designed for rapid simulation, implementation, and verification of video and image processing algorithms and systems.
An Adaptive Motion Model and Multi-feature Cues Based on Particle Filter for Object Tracking
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Full Text Available If there is an occlusion, the target state model would not match motion model anymore and measurement model also would get worse. To solve these problems, an improved particle filtering algorithm based on adaptive motion model and multiple-cue fusion is presented. Under the theory framework of particle filters, the weighted color histogram and LBP texture feature entropy are used to describe features. And the algorithm adjusts features distribution coefficient automatically by calculating the Bhattacharyya distance between the object reference distribution and object sample distribution, thus the color and texture features can intelligently be fused to develop the observation model. The simple second order auto regressive model is chosen as the state transition model, and the system noise variance is adaptively determined by the minimum noise variance of every feature in object tracking. For the occlusion problem, the system maximum noise variance can be selected, along with particle random motion intensified and disseminating coverage amplified. The posterior distribution of the object is approximated by a set of weighted samples, while object tracking is implemented by the Bayesian propagation of the sample set. The analyses and experiments show that the performance of the proposed method is more effective and robust to target maneuver and occlusion and has good performances under complex background.
Ming Li
Object Tracking with an Evolutionary Particle Filter Based on Self-Adaptive Multi-Features Fusion
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Full Text Available Particle filter algorithms are widely used for object tracking in video sequences, but the standard particle filter algorithm cannot solve the validity of particles ideally. To solve the problems of particle degeneration and sample impoverishment in a particle filter tracking algorithm, an improved object tracking algorithm is proposed, which combines a multi?feature fusion method and a genetic evolution mechanism. The algorithm dynamically computes the feature's fusion weight by the discriminability of each vision feature and then constructs the important density function based on selecting a feature's fusion method adaptively. Moreover, a self?adaptive genetic evolutionary mechanism is introduced into the particle resampling process and makes the particle become an agent with the ability of dynamic self?adaption. With self?adaptive crossover and mutation operators, the evolution system produces a large number of new particles, which can better approximate the true state of the tracking object. The experimental results show that the proposed object tracking algorithm surpasses the conventional particle filter on both robustness and accuracy, even though the tracking object is very challenging regarding illumination variation, structural deformation, the interference of similar targets and occlusion.
Zhang Xiaowei
Diversity Based on Entropy: A Novel Evaluation Criterion in Multi-objective Optimization Algorithm
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Full Text Available Quality assessment of Multi-objective Optimization algorithms has been a major concern in the scientific field during the last decades. The entropy metric is introduced and highlighted in computing the diversity of Multi-objective Optimization Algorithms. In this paper, the definition of the entropy metric and the approach of diversity measurement based on entropy are presented. This measurement is adopted to not only Multi-objective Evolutionary Algorithm but also Multi-objective Immune Algorithm. Besides, the key techniques of entropy metric, such as the appropriate principle of grid method, the reasonable parameter selection and the simplification of density function, are discussed and analyzed. Moreover, experimental results prove the validity and efficiency of the entropy metric. The computational effort of entropy increases at a linear rate with the number of points in the solution set, which is indeed superior to other quality indicators. Compared with Generational Distance, it is proved that the entropy metric have the capability of describing the diversity performance on a quantitative basis. Therefore, the entropy criterion can serve as a high-efficient diversity criterion of Multi-objective optimization algorithms.
Wang LinLin
Three-dimensional model-based object recognition and segmentation in cluttered scenes.
Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency. PMID:16986541
Mian, Ajmal S; Bennamoun, Mohammed; Owens, Robyn
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Full Text Available In this work, the potential of Advanced Land Observing Satellite (ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR imagery to map burned areas was evaluated in two study areas in Greece. For this purpose, we developed an object-based classification scheme to map the fire-disturbed areas using the PALSAR imagery acquired before and shortly after fire events. The advantage of employing an object-based approach was not only the use of the temporal variation of the backscatter coefficient, but also the incorporation in the classification of topological features, such as neighbor objects, and class related features, such as objects classified as burned. The classification scheme resulted in mapping the burned areas with satisfactory results: 0.71 and 0.82 probabilities of detection for the two study areas. Our investigation revealed that the pre-fire vegetation conditions and fire severity should be taken in consideration when mapping burned areas using PALSAR in Mediterranean regions. Overall, findings suggest that the developed scheme could be applied for rapid burned area assessment, especially to areas where cloud cover and fire smoke inhibit accurate mapping of burned areas when optical data are used.
Anastasia Polychronaki
Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain
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Full Text Available In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR, Structural Similarity Index (SSIM and the Visual Information Fidelity (VIF. The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided.
M. Cedillo-Hernandez
Recovering Use Case Diagrams from Object Oriented Code: an MDA-based Approach
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Full Text Available Modernization of legacy systems requires the existence of technicalframeworks for information integration and tool interoperability that allow managing new platform technologies, design techniques and processes. MDA (Model Driven Architecture, adopted by the OMG (Object Management Group, is aligned with this requirement. Reverse engineering techniques play a crucial role in system modernization. In light of these issues, this article describes a framework to reverse engineering MDA models from object oriented code. This framework distinguishes three different abstraction levelslinked to models, metamodels and formal specifications. At model level,transformations are based on static and dynamic analysis. At metamodel level, transformations are specified as OCL (Object Constraint Languagecontracts between MOF (Meta Object Facility metamodels which control the consistency of these transformations. The level of formal specification includes algebraic specifications of MOF metamodels and metamodel-based transformations. This article shows how to reverse engineering use case diagrams from Java code in the MDA context focusing on transformations at model and metamodel levels. We validate our approach by using Eclipse Modeling Framework, Ecore metamodels and ATL (Atlas Transformation Language.
Claudia T. Pereira
Object detection and tracking method of AUV based on acoustic vision
This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.
Zhang, Tie-dong; Wan, Lei; Zeng, Wen-jing; Xu, Yu-ru
Internet of Things based on smart objects technology, middleware and applications
The Internet of Things (IoT) usually refers to a world-wide network of interconnected heterogeneous objects (sensors, actuators, smart devices, smart objects, RFID, embedded computers, etc) uniquely addressable, based on standard communication protocols. Beyond such a definition, it is emerging a new definition of IoT seen as a loosely coupled, decentralized system of cooperating smart objects (SOs). A SO is an autonomous, physical digital object augmented with sensing/actuating, processing, storing, and networking capabilities. SOs are able to sense/actuate, store, and interpret information created within themselves and around the neighbouring external world where they are situated, act on their own, cooperate with each other, and exchange information with other kinds of electronic devices and human users. However, such SO-oriented IoT raises many in-the-small and in-the-large issues involving SO programming, IoT system architecture/middleware and methods/methodologies for the development of SO-based applica...
Trunfio, Paolo
Digital Repository Infrastructure Vision for European Research (DRIVER)
We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values i...
Harini Nagendra; Shivani Agarwal; Madhumitha Jaganmohan; Lionel Sujay Vailshery
Unified web-based network management based on distributed object orientated software agents
This paper presents an architecture that provides a unified web interface to managed network devices that support CORBA, OSI or Internet-based network management protocols. A client gains access to managed devices through a web browser, which is used to issue management operations and receive event notifications. The proposed architecture is compatible with both the OSI Management reference Model and CORBA. The steps required for designing the building blocks of such architecture are identified.
Djalalian, Amir; Mukhtar, Rami; Zukerman, Moshe
Performance Evaluation of RTLS Based on Active RFID Power Measurement for Dense Moving Objects
Tracking a large quantity of moving target tags simultaneously is essential for the localization and guidance of people in welfare facilities like hospitals and sanatoriums for the aged. The locating system using active RFID technology consists of a number of fixed RFID readers and tags carried by the target objects, or senior people. We compare the performances of several determination algorithms which use the power measurement of received signals emitted by the moving active RFID tags. This letter presents a study on the effect of collision in tracking large quantities of objects based on active RFID real time location system (RTLS). Traditional trilateration, fingerprinting, and well-known LANDMARC algorithm are evaluated and compared with varying number of moving tags through the SystemC-based computer simulation. From the simulation, we show the tradeoff relationship between the number of moving tags and estimation accuracy.
Kim, Taekyu; Lee, Jin; Lee, Seungbeom; Park, Sin-Chong
In this paper we present the functionality of a currently under development database programming methodology called ODRA (Object Database for Rapid Application development) which works fully on the object oriented principles. The database programming language is called SBQL (Stack based query language). We discuss some concepts in ODRA for e.g. the working of ODRA, how ODRA runtime environment operates, the interoperability of ODRA with .net and java .A view of ODRA's working with web services and xml. Currently the stages under development in ODRA are query optimization. So we present the prior work that is done in ODRA related to Query optimization and we also present a new fusion algorithm of how ODRA can deal with joins based on collections like set, lists, and arrays for query optimization.
Satish, Laika
Empirical analysis of web-based user-object bipartite networks
Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative similarity, to quantify the diversity of tastes based on the collaborative selection. Accordingly, the correlation between degree and selection diversity is investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.
Shang, Ming-Sheng; Lü, Linyuan; Zhang, Yi-Cheng; Zhou, Tao
Multi-objective Flexible Scheduling Optimization Scheme base on Improved DNA Genetic Algorithm
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Full Text Available In this paper, established a mathematical model for multi-objective flexible scheduling problems, combined Pareto non-dominated sorting method, put forward a hybrid genetic algorithm based on improved DNA computation. To ensure the diversity of the optimal solution sets, designed RNA quaternary encoder mode and genetic operator based on improved DNA computation, adopted sub-area crossover and dynamic mutation, imposed on manipulation of the molecular level. Through simulation, tested the performance of the designed algorithm, compared it with the standard genetic algorithm test results. Simulation results showed the proposed algorithm can provide an optimum searching, owned better seeking abilities; the obtained scheduling results were fairly reasonable. This algorithm can effectively solve the multi-objective flexible scheduling optimization problems.
Shuzhi Nie
Digital Repository Infrastructure Vision for European Research (DRIVER)
This research project aims to further explore an object oriented methodology in which a hydrological system is considered to be a series of interacting hydrological elements. It will extend Slingsby's hydrological model TINMOD (2002) whose data structure is based on a TIN with embedded methods and behaviours to build, maintain and derive its topology – as well as derive hydrological information (flow-paths, basins, flow length) about itself. Specifically, this project aims to add functi...
Stedham, Rl
Digital Repository Infrastructure Vision for European Research (DRIVER)
Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub...
Jane Southworth; Youliang Qiu; Luke Rostant; Sanchayeeta Adhikari; Cerian Gibbes
Pixel-based and object-oriented change detection analysis using high-resolution imagery
International Nuclear Information System (INIS)
The high spatial resolution of state-of-the-art commercial satellite imagery provides a good basis for recognising and monitoring even small-scale structural changes within nuclear facilities and for planning of routine and/or challenge inspections of nuclear sites. Despite the advantages of the improved spatial resolution some problems exist that may make the interpretation of the changes more difficult: Firstly, the results of the change analysis can be very complex and unclear at a glance. Secondly, shadow formation and off-nadir images due to different sensor and solar conditions at the acquisition times can cause false signals or overlap real changes. In view to the fast-growing amount of data from different sensor types there are then some requirements of an effective change detection procedure for safeguards purposes: i. The techniques involved should possess a certain amount of robustness in terms of small misregistration errors, different atmospheric conditions at the acquiring dates, off-nadir angles. ii. Given large multisensor data sets it would be necessary that the procedure operates as automatically as possible. Iii. The procedure has to imply techniques to initially pinpoint out those parts of a scene in which significant changes have taken place. iv. All image areas indicating changes then should be subject to a detailed classification and interpretation procedure. We have already investigated some pixel-based change detection for the routine nuclear verification, based on recently published visualisation and change detection algorithms: canonical correlation analysis (MAD transformation) to enhance the change information in the difference images and bayesian techniques for the automatic determination of significant thresholds. Some steps have been taken by combining pixel- and object-oriented approaches, i.e. MAD transformation of the image data and object- oriented post-classification of the changes and object extraction for the image data or MAD transformation of the objects and post-classification of the change objects. Another approach implies a solely object-oriented change detection technique: Object extraction, semantic classification and post- classification comparison by means of a change matrix. The object-oriented change analysis procedures are carried out with a relatively new technology for image analysis, eCognition (http://www.definiens-imaging.com)
An object-based approach to weather analysis and its applications
The research group 'Object-based Analysis and SEamless prediction' (OASE) within the Hans Ertel Centre for Weather Research programme (HErZ) pursues an object-based approach to weather analysis. The object-based tracking approach adopts the Lagrange perspective by identifying and following the development of convective events over the course of their lifetime. Prerequisites of the object-based analysis are a high-resolved observational data base and a tracking algorithm. A near real-time radar and satellite remote sensing-driven 3D observation-microphysics composite covering Germany, currently under development, contains gridded observations and estimated microphysical quantities. A 3D scale-space tracking identifies convective rain events in the dual-composite and monitors the development over the course of their lifetime. The OASE-group exploits the object-based approach in several fields of application: (1) For a better understanding and analysis of precipitation processes responsible for extreme weather events, (2) in nowcasting, (3) as a novel approach for validation of meso-? atmospheric models, and (4) in data assimilation. Results from the different fields of application will be presented. The basic idea of the object-based approach is to identify a small set of radar- and satellite derived descriptors which characterize the temporal development of precipitation systems which constitute the objects. So-called proxies of the precipitation process are e.g. the temporal change of the brightband, vertically extensive columns of enhanced differential reflectivity ZDR or the cloud top temperature and heights identified in the 4D field of ground-based radar reflectivities and satellite retrievals generated by a cell during its life time. They quantify (micro-) physical differences among rain events and relate to the precipitation yield. Analyses on the informative content of ZDR columns as precursor for storm evolution for example will be presented to demonstrate the use of such system-oriented predictors for nowcasting. Columns of differential reflectivity ZDR measured by polarimetric weather radars are prominent signatures associated with thunderstorm updrafts. Since greater vertical velocities can loft larger drops and water-coated ice particles to higher altitudes above the environmental freezing level, the integrated ZDR column above the freezing level increases with increasing updraft intensity. Validation of atmospheric models concerning precipitation representation or prediction is usually confined to comparisons of precipitation fields or their temporal and spatial statistics. A comparison of the rain rates alone, however, does not immediately explain discrepancies between models and observations, because similar rain rates might be produced by different processes. Within the event-based approach for validation of models both observed and modeled rain events are analyzed by means of proxies of the precipitation process. Both sets of descriptors represent the basis for model validation since different leading descriptors - in a statistical sense- hint at process formulations potentially responsible for model failures.
Troemel, Silke; Diederich, Malte; Horvath, Akos; Simmer, Clemens; Kumjian, Matthew
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Full Text Available moving objects detection is a fundamental step in many vision based applications. Background subtraction is the typical method. Many background models have been introduced to deal with different problems. The method based on mixture of Gaussians is a good balance between accuracy and complexity, and is used frequently by many researchers. But it still cannot provide satisfied results in some cases. In this paper, we solve this problem by introducing a post process to the initial results of mixture of Gaussians method. An over-segmentation based on color information is used to segment the input frame into patches. The goal of segmentation is to split each image into regions that are likely to belong to the same object. After moving shadow suppression, the outputs of mixture of Gaussians are combined with the color clustered regions to a module for area confidence measurement. In this way, two major segment errors can be corrected. Finally, by connected component labeling, blobs with too small area are filter out, and the contour of moving objects are extracted. Experimental results show that the proposed approach can significantly enhance segmentation results.
yun-fang zhu
Object-Based Image Analysis of Downed Logs in Disturbed Forested Landscapes Using Lidar
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Full Text Available Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst's time in the delineation and classification of downed logs in the lidar data.
Maggi Kelly
An advanced object-based software framework for complex ecosystem modeling and simulation
Energy Technology Data Exchange (ETDEWEB)
Military land managers and decision makers face an ever increasing challenge to balance maximum flexibility for the mission with a diverse set of multiple land use, social, political, and economic goals. In addition, these goals encompass environmental requirements for maintaining ecosystem health and sustainability over the long term. Spatiotemporal modeling and simulation in support of adaptive ecosystem management can be best accomplished through a dynamic, integrated, and flexible approach that incorporates scientific and technological components into a comprehensive ecosystem modeling framework. The Integrated Dynamic Landscape Analysis and Modeling System (IDLAMS) integrates ecological models and decision support techniques through a geographic information system (GIS)-based backbone. Recently, an object-oriented (OO) architectural framework was developed for IDLAMS (OO-IDLAMS). This OO-IDLAMS Prototype was built upon and leverages from the Dynamic Information Architecture System (DIAS) developed by Argonne National Laboratory. DIAS is an object-based architectural framework that affords a more integrated, dynamic, and flexible approach to comprehensive ecosystem modeling than was possible with the GIS-based integration approach of the original IDLAMS. The flexibility, dynamics, and interoperability demonstrated through this case study of an object-oriented approach have the potential to provide key technology solutions for many of the military's multiple-use goals and needs for integrated natural resource planning and ecosystem management.
Sydelko, P. J.; Dolph, J. E.; Majerus, K. A.; Taxon, T. N.
Glandular object based tumor morphometry in H&E biopsy samples for prostate cancer prognosis
Morphological and architectural characteristics of primary prostate tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide critical information for cancer diagnosis, prognosis and therapeutic response prediction. The subjective and variable Gleason grade assessed by expert pathologists in Hematoxylin and Eosin (H&E) stained specimens has been the standard for prostate cancer diagnosis and prognosis. We propose a novel morphometric, glandular object-oriented image analysis approach for the robust quantification of H&E prostate biopsy images. We demonstrate the utility of features extracted through the proposed method in predicting disease progression post treatment in a multi-institution cohort of 1027 patients. The biopsy based features were univariately predictive for clinical response post therapy; with concordance indexes (CI) = 0.6. In multivariate analysis, a glandular object feature quantifying tumor epithelial cells not directly associated with an intact tumor gland was selected in a model incorporating preoperative clinical data, protein biomarker and morphological imaging features. The model achieved a CI of 0.73 in validation, which was significantly higher than a CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice. This work presents one of the first demonstrations of glandular object based morphological features in the H&E stained biopsy specimen to predict disease progression post primary treatment. Additionally, it is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.
Fogarasi, Stephen I.; Khan, Faisal M.; Pang, Ho-Yuen H.; Mesa-Tejada, Ricardo; Donovan, Michael J.; Fernandez, Gerardo
Object-Based Image Classification of Summer Crops with Machine Learning Methods
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Full Text Available The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR, support vector machine (SVM and multilayer perceptron (MLP neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86% and notably higher than C4.5 (79%. The SVM+SVM classifier (best method improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity. The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.
José M. Peńa
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Full Text Available With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.
Mr.D. V. Kodavade
Multi-objective optimum design of fast tool servo based on improved differential evolution algorithm
International Nuclear Information System (INIS)
The flexure-based mechanism is a promising realization of fast tool servo (FTS), and the optimum determination of flexure hinge parameters is one of the most important elements in the FTS design. This paper presents a multi-objective optimization approach to optimizing the dimension and position parameters of the flexure-based mechanism, which is based on the improved differential evolution algorithm embedding chaos and nonlinear simulated anneal algorithm. The results of optimum design show that the proposed algorithm has excellent performance and a well-balanced compromise is made between two conflicting objectives, the stroke and natural frequency of the FTS mechanism. The validation tests based on finite element analysis (FEA) show good agreement with the results obtained by using the proposed theoretical algorithm of this paper. Finally, a series of experimental tests are conducted to validate the design process and assess the performance of the FTS mechanism. The designed FTS reaches up to a stroke of 10.25 ?m with at least 2 kHz bandwidth. Both of the FEA and experimental results demonstrate that the parameters of the flexure-based mechanism determined by the proposed approaches can achieve the specified performance and the proposed approach is suitable for the optimum design of FTS mechanism and of excellent performances
An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images
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Full Text Available There have been increasing demands for automatically monitoring urban areas in very high detail, and the Unmanned Aerial Vehicle (UAV with auto-navigation (AUNA system offers such capability. This study proposes an object-based hierarchical method to detect changes from UAV images taken at different times. It consists of several steps. In the first step, an octocopter with AUNA capability is used to acquire images at different dates. These images are registered automatically, based on SIFT (Scale-Invariant Feature Transform feature points, via the general bundle adjustment framework. Thus, the Digital Surface Models (DSMs and orthophotos can be generated for raster-based change analysis. In the next step, a multi-primitive segmentation method combining the spectral and geometric information is proposed for object-based analysis. In the final step, a multi-criteria decision analysis is carried out concerning the height, spectral and geometric coherence, and shape regularity for change determination. Experiments based on UAV images with five-centimeter ground resolution demonstrate the effectiveness of the proposed method, leading to the conclusion that this method is practically applicable for frequent monitoring.
Rongjun Qin
In this paper, we address a highly enhanced compression scheme in the condition of multiple objects in Integral Imaging (InIm) by use of sub-images (SIs) to segment each object and to remove the Motion Vector (MV) of residual image array transformed from Sub-Image Array (SIA). In the pick-up process, SIA is generated from EIA after the perspectives passing through virtual pinhole array is recorded as Elemental Image Array (EIA). The similarity enhancement among SIs expects compression efficiency to improve, but the compression efficiency of the EIA in the picked-up condition of multiple objects does not correspond to that of the picked-up condition of a simplified object. In the proposed scheme, the depth of objects is computed by two adaptive SIs located at horizontal left and right side from the reference SI positioned to the center of the SIA. A depth map image generated from two adaptive the SIs and a reference SI is applied to segment each object considering to the distance between those. Therefore, an adaptive objectsegmented SI is obtained and, which is motion-estimated from the original SIA based on MSE to generate the motioncompensated object-segmented SIA and which SIAs from each segmented object are finally combined as the motioncompensated SIA, and which based on multiple objects is transformed to residual SIA to minimize the spatial redundancy and which SIA is compressed by MPEG-4. The proposed algorithm shows the enhanced compression efficiency than that of the baseline JPEG and the conventional EIA compression scheme.
Lee, Hyoung-Woo; Lee, Ju-Han; Kang, Ho-Hyun; Kim, Eun-Soo
How a face may affect object-based attention: Evidence from adults and 8-month-old infants
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Full Text Available Object-based attention operates on perceptual objects, opening the possibility that the costs and benefits humans have to pay to move attention between objects might be affected by the nature of the stimuli. The current study reported two experiments with adults and 8-month-old infants investigating whether object-based-attention is affected by the stimulus social salience (faces vs. non-faces stimuli. Using the well-known cueing task developed by Egly et al. (1994 to study the object-based component of attention, in Experiment 1 adult participants were presented with two upright, inverted or scrambled faces and an eye-tracker measured their saccadic latencies to find a target that could appear on the same object that was just cued or on the other object that was uncued. Data showed that an object-based effect (a minor cost to shift attention within- compared to between-objects occurred only with scrambled face, but not with upright or inverted faces. In Experiment 2 the same task was performed with 8-month-old infants, using upright and inverted faces. Data revealed that an object-based effect only emerges for inverted faces but not for upright faces. Overall, these findings suggest that object-based attention is modulated by the stimulus social salience and by the experience acquired by the viewer with different objects.
Eloisa Valenza
The existing research of steering comfort mainly focuses on the subjective evaluation, aiming at designing and optimizing the steering system. In the development of steering system, especially the evaluation of steering comfort, the objective evaluation methods considered the kinematic characteristics of driver steering maneuver are not proposed, which means that the objective evaluation of steering cannot be conducted with the evaluation of kinematic characteristics of driver in steering maneuver. In order to propose the objective evaluation methods of steering comfort, the evaluation of steering movement quality of driver is developed on the basis of the study of the kinematic characteristics of steering maneuver. First, the steering motion trajectories of the driver in both comfortable and certain extreme uncomfortable operation conditions are detected using the Vicon motion capture system. The operation conditions are under the restrictions of the vertical height and horizontal distance between steering wheel center and the H-point of driver, and the steering resisting torque else. Next, the movement quality evaluation of driver steering maneuver is assessed using twelve kinds of evaluation indices based on the kinematic analyses of the steering motion trajectories to propose an objective evaluation method. Finally, an integrated discomfort index of steering maneuver is proposed on the basis of the regression analysis of subjective evaluation rating and the movement quality evaluation indices, including the Jerk, Discomfort and Joint Torque indices. The test results show that the proposed integrated discomfort index gives a good fitting with the subjective evaluation of discomfort, which means it can be used to evaluate or predict the discomfort level of steering maneuver. This paper proposes an objective evaluation method of steering comfort based on the movement quality evaluation of driver steering maneuver.
Yang, Yiyong; Liu, Yahui; Wang, Man; Ji, Run; Ji, Xuewu
Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems.
Automated motion detection, which segments moving objects from video streams, is the key technology of intelligent transportation systems for traffic management. Traffic surveillance systems use video communication over real-world networks with limited bandwidth, which frequently suffers because of either network congestion or unstable bandwidth. Evidence supporting these problems abounds in publications about wireless video communication. Thus, to effectively perform the arduous task of motion detection over a network with unstable bandwidth, a process by which bit-rate is allocated to match the available network bandwidth is necessitated. This process is accomplished by the rate control scheme. This paper presents a new motion detection approach that is based on the cerebellar-model-articulation-controller (CMAC) through artificial neural networks to completely and accurately detect moving objects in both high and low bit-rate video streams. The proposed approach is consisted of a probabilistic background generation (PBG) module and a moving object detection (MOD) module. To ensure that the properties of variable bit-rate video streams are accommodated, the proposed PBG module effectively produces a probabilistic background model through an unsupervised learning process over variable bit-rate video streams. Next, the MOD module, which is based on the CMAC network, completely and accurately detects moving objects in both low and high bit-rate video streams by implementing two procedures: 1) a block selection procedure and 2) an object detection procedure. The detection results show that our proposed approach is capable of performing with higher efficacy when compared with the results produced by other state-of-the-art approaches in variable bit-rate video streams over real-world limited bandwidth networks. Both qualitative and quantitative evaluations support this claim; for instance, the proposed approach achieves Similarity and F1 accuracy rates that are 76.40% and 84.37% higher than those of existing approaches, respectively. PMID:24805212
Huang, Shih-Chia; Chen, Bo-Hao
Pixel VS Object-Based Image Classification Techniques for LIDAR Intensity Data
Light Detection and Ranging (LiDAR) systems are remote sensing techniques used mainly for terrain surface modelling. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which provides high separability in the reflected energy by the target. This phenomenon is investigated to use the LiDAR intensity data for land-cover classification. The goal of this paper is to investigate and evaluates the use of different image classification techniques applied on LiDAR intensity data for land cover classification. The two techniques proposed are: a) Maximum likelihood classifier used as pixel- based classification technique; and b) Image segmentation used as object-based classification technique. A study area covers an urban district in Burnaby, British Colombia, Canada, is selected to test the different classification techniques for extracting four feature classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas, from the LiDAR intensity data. Generally, the results show that LiDAR intensity data can be used for land cover classification. An overall accuracy of 63.5% can be achieved using the pixel-based classification technique. The overall accuracy of the results is improved to 68% using the object- based classification technique. Further research is underway to investigate different criteria for segmentation process and to refine the design of the object-based classification algorithm.
El-Ashmawy, N.; Shaker, A.; Yan, W.
MEMS-based programmable reflective slit mask for multi-object spectroscopy
Multi-object spectroscopy is a powerful tool for space and ground-based telescopes for the study of the formation of galaxies. This technique requires a programmable slit mask for astronomical object selection. We are developing MEMS-based programmable reflective slit masks for multi-object spectroscopy that consist of micromirror arrays on which each micromirror of size 100 x 200 ?m2 is electrostatically tilted providing a precise angle. The main requirements for these arrays are cryogenic environment capabilities, precise and uniform tilt angle over the whole device, uniformity of the mirror voltage-tilt hysteresis and a low mirror deformation. A first generation of MEMS-based programmable reflective slit masks composed of 5 x 5 micromirrors was tested in cryogenic conditions at 92 K. Then, first prototypes of large arrays were microfabricated and characterized, but the reliability of these arrays had to be improved. To increase the reliability of these devices, a third generation of micromirror arrays composed of 64 x 32 micromirrors is under development. This generation was especially designed for individual actuation of each mirror, applying a line-column algorithm based on the voltage-tilt hysteresis of the actuator. The fabrication process was optimized and is now based on multiple wafer level bonding steps. Microfabricated devices have micromirror with a peak-to-valley deformation less than 3 nm. The mirrors can be tilted at 20° by an actuation voltage lower than 100 V. First experiments showed that our micromirrors are well suited for the line-column addressing of each micromirror.
Canonica, Michael; Zamkotsian, Frederic; Lanzoni, Patrick; Noell, Wilfried; de Rooij, Nico
Whisker-based object localization requires activation and plasticity of somatosensory and motor cortex. These parts of the cerebral cortex receive strong projections from the cerebellum via the thalamus, but it is unclear whether and to what extent cerebellar processing may contribute to such a sensorimotor task. Here, we subjected knock-out mice, which suffer from impaired intrinsic plasticity in their Purkinje cells and long-term potentiation at their parallel fiber-to-Purkinje cell synapses (L7-PP2B), to an object localization task with a time response window (RW). Water-deprived animals had to learn to localize an object with their whiskers, and based upon this location they were trained to lick within a particular period ("go" trial) or refrain from licking ("no-go" trial). L7-PP2B mice were not ataxic and showed proper basic motor performance during whisking and licking, but were severely impaired in learning this task compared with wild-type littermates. Significantly fewer L7-PP2B mice were able to learn the task at long RWs. Those L7-PP2B mice that eventually learned the task made unstable progress, were significantly slower in learning, and showed deficiencies in temporal tuning. These differences became greater as the RW became narrower. Trained wild-type mice, but not L7-PP2B mice, showed a net increase in simple spikes and complex spikes of their Purkinje cells during the task. We conclude that cerebellar processing, and potentiation in particular, can contribute to learning a whisker-based object localization task when timing is relevant. This study points toward a relevant role of cerebellum-cerebrum interaction in a sophisticated cognitive task requiring strict temporal processing. PMID:24478374
Rahmati, Negah; Owens, Cullen B; Bosman, Laurens W J; Spanke, Jochen K; Lindeman, Sander; Gong, Wei; Potters, Jan-Willem; Romano, Vincenzo; Voges, Kai; Moscato, Letizia; Koekkoek, Sebastiaan K E; Negrello, Mario; De Zeeuw, Chris I
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Object recognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the object recognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D object recognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matching.
Guerra, C; Pascucci, V
A Hybrid Simplex Multi-Objective Evolutionary Algorithm Based on A New Fitness Assignment Strategy
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Full Text Available In multi-objective evolutionary algorithms (MOEAs, the traditional fitness assignment strategy based on Pareto dominance is ineffective in sorting out the high-quality solutions when the number of the objective is large. Recently, many scholars have used preference order (PO ranking approach as an optimality criterion in the ranking stage of MOEAs. The experiment shows that the algorithms equipped with the PO ranking procedures can have a better convergence to the true Pareto surface, but are ineffective to maintain a set of well-distributed solutions over the Pareto surface. In order to overcome above shortcomings, a new algorithm is proposed which adopts a new fitness assignment strategy using the information of the individual preference order ranking and the individual density. In this way, it is helpful to guide the individuals to more sparse areas in the Pareto Front. At the same time, the proposed algorithm effectively combines multi-objective evolutionary algorithm with the Nelder-Mead simplex search to get a balance between the exploration and exploitation abilities. In each generation, the algorithm adopts a parallel hybrid way to evolve two subsets simultaneously, and the population will be improved by both evolution algorithm and simplex search. The proposed algorithm has been compared with other MOEAs on some many-objective problems by experiments. The experimental results indicate that the proposed algorithm achieves a better performance in convergence and diversity.
Xiaofang Guo
An effective docking strategy for virtual screening based on multi-objective optimization algorithm
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Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.
Kang Ling
Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking
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Full Text Available Nonlinear object tracking from noisy measurements is a basic skill and a challenging task of mobile robotics, especially under dynamic environments. The particle filter is a useful tool for nonlinear object tracking with non?Gaussian noise. Nonlinear object tracking needs the real?time processing capability of the particle filter. While the number in a traditional particle filter is fixed, that can lead to a lot of unnecessary computation. To address this issue, a confidence?level? based new adaptive particle filter (NAPF algorithm is proposed in this paper. In this algorithm the idea of confidence interval is utilized. The least number of particles for the next time instant is estimated according to the confidence level and the variance of the estimated state. Accordingly, an improved systematic re?sampling algorithm is utilized for the new improved particle filter. NAPF can effectively reduce the computation while ensuring the accuracy of nonlinear object tracking. The simulation results and the ball tracking results of the robot verify the effectiveness of the algorithm.
Xiaoyong Zhang
Multi-object spectroscopy (MOS) is a powerful tool for space and ground-based telescopes for studying the formation of galaxies. This technique requires a programmable slit mask for astronomical object selection. A first sample of MEMS-based programmable reflective slit masks with elements of size 200×100 ?m2 has been successfully tested in cryogenic conditions at 92 K. Devices of larger size were microfabricated, the largest chip measures 25×22 mm2 and is composed of 200×100 electrostatic actuated micromirrors. These devices are composed of two chips: the electrode chip and the mirror chip, which are processed separately and assembled consecutively. The mirror chip is bonded on top of the electrode chip and microfabricated pillars on the electrode chip provide the necessary spacing between the two parts. A process flow utilizing refilling techniques based on borophosphosilicate glass (BPSG) deposition and reflow was developed. Programmable reflective slit masks based on this fabrication process were microfabricated and characterized. These devices exhibit a micromirror deformation of 11 nm peak-to-valley and an actuation voltage of 145 V for a tilt angle of 9°. Preparation of samples for MOS experiments are underway.
Canonica, Michael; Waldis, Severin; Zamkotsian, Frederic; Lanzoni, Patrick; Noell, Wilfried; de Rooij, Nico
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Full Text Available This paper proposed an algorithm to track the obstacle position and avoid the moving objects for differential driving Automatic Guided Vehicles (AGV system in industrial environment. This algorithm has several abilities such as: to detect the moving objects, to predict the velocity and direction of moving objects, to predict the collision possibility and to plan the avoidance maneuver. For sensing the local environment and positioning, the laser measurement system LMS-151 and laser navigation system NAV-200 are applied. Based on the measurement results of the sensors, the stationary and moving obstacles are detected and the collision possibility is calculated. The velocity and direction of the obstacle are predicted using Kalman filter algorithm. Collision possibility, time, and position can be calculated by comparing the AGV movement and obstacle prediction result obtained by Kalman filter. Finally the avoidance maneuver using the well known tangent Bug algorithm is decided based on the calculation data. The effectiveness of proposed algorithm is verified using simulation and experiment. Several examples of experiment conditions are presented using stationary obstacle, and moving obstacles. The simulation and experiment results show that the AGV can detect and avoid the obstacles successfully in all experimental condition.
Pandu Sandi Pratama
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Full Text Available This paper proposed an algorithm to track the obstacle position and avoid the moving objects for differential driving Automatic Guided Vehicles (AGV system in industrial environment. This algorithm has several abilities such as: to detect the moving objects, to predict the velocity and direction of moving objects, to predict the collision possibility and to plan the avoidance maneuver. For sensing the local environment and positioning, the laser measurement system LMS-151 and laser navigation system NAV-200 are applied. Based on the measurement results of the sensors, the stationary and moving obstacles are detected and the collision possibility is calculated. The velocity and direction of the obstacle are predicted using Kalman filter algorithm. Collision possibility, time, and position can be calculated by comparing the AGV movement and obstacle prediction result obtained by Kalman filter. Finally the avoidance maneuver using the well known tangent Bug algorithm is decided based on the calculation data. The effectiveness of proposed algorithm is verified using simulation and experiment. Several examples of experiment conditions are presented using stationary obstacle, and moving obstacles. The simulation and experiment results show that the AGV can detect and avoid the obstacles successfully in all experimental condition. [Keywords— Obstacle avoidance, AGV, differential drive, laser measurement system, laser navigation system].
Pandu Sandi Pratama
Remote Sensing in Mapping Mangrove Ecosystems — An Object-Based Approach
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Full Text Available Over the past few decades, clearing for shrimp farming has caused severe losses of mangroves in the Mekong Delta (MD of Vietnam. Although the increasing importance of shrimp aquaculture in Vietnam has brought significant financial benefits to the local communities, the rapid and largely uncontrolled increase in aquacultural area has contributed to a considerable loss of mangrove forests and to environmental degradation. Although different approaches have been used for mangrove classification, no approach to date has addressed the challenges of the special conditions that can be found in the aquaculture-mangrove system in the Ca Mau province of the MD. This paper presents an object-based classification approach for estimating the percentage of mangroves in mixed mangrove-aquaculture farming systems to assist the government to monitor the extent of the shrimp farming area. The method comprises multi-resolution segmentation and classification of SPOT5 data using a decision tree approach as well as local knowledge from the region of interest. The results show accuracies higher than 75% for certain classes at the object level. Furthermore, we successfully detect areas with mixed aquaculture-mangrove land cover with high accuracies. Based on these results, mangrove development, especially within shrimp farming-mangrove systems, can be monitored. However, the mangrove forest cover fraction per object is affected by image segmentation and thus does not always correspond to the real farm boundaries. It remains a serious challenge, then, to accurately map mangrove forest cover within mixed systems.
Quoc Tuan Vo
A new target-oriented parameterization scheme, named the object-based model, is suggested to represent man-made or natural targets as regular shapes embedded in a two-dimensional resistivity background. The numerical values of the target parameters (size, depth, location and resistivity) are estimated in three steps consisting of conventional regularized inversion, exclusion of anomalous regions and delineation of target bodies. The method produces sharp edges and sharp variation in intrinsic resistivity between the targets and background. The number of target objects is decided by the visual inspection of the 2D resistivity section derived from the application of a conventional cell-based regularized inversion. The 2D background is also extracted from the same section. A genetic algorithm approach is used at the final stage to test a large number of distinct models. Each test model consists of the same number of objects buried in the 2D background. The size, depth, location and resistivity of the targets are estimated from a class of models generated by the application of biological rules. The derived images of buried bodies have sharp edges and can then be understood by engineers and archeologists. However, if the hypothesis about the 'conceptual model' is very different from the geometry of the subsurface, the proposed approach will not be able to produce satisfactory results.
Ba?okur, Ahmet T.; Akca, Irfan
Multi-objective scheduling in an agent based Holonic manufacturing system
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Full Text Available The present paper is aimed at multi-objective scheduling in an agent based holonic manufacturing system to satisfy the goal of several communities namely the product, the resource, and the organization simultaneously. In this attempt, first a multi criteria based priority rule is developed following Simple Additive Weight (SAW method under Multi Criteria Decision Making (MCDM environment to rank the products. Accordingly, the products are allowed to select a particular resource for execution by negotiation considering minimum time as criterion. The interests of different communities are accomplished by allocating the ordered rank of products to the ordered rank of resources. Conflict, if arises between products and resources, are resolved by introducing the concept of Early Finish Time (EFT as criterion for task allocation. A scheduling algorithm is proposed for execution of the rule. In view of machine failure, a cooperation strategy is evolved that also optimizes reallocation of the incomplete task. It is concluded that the proposed scheduling algorithm together with the disturbance handling algorithm are poised to satisfy the agent's local objective as well as organization's global objective concurrently and are commensurable with multi agent paradigm.
T. K. Jana
Reference Point Based Multi-Objective Optimization Using Hybrid Artificial Immune System
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Full Text Available During the last decade, the field of Artificial Immune System (AIS is progressing slowly and steadily as a branch of Computational Intelligence (CI.There has been increasing interest in the development of computational models inspired by several immunological principles. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Pareto optimal solution is also an important task. In this paper, a Reference Point Based Multi-Objective Optimization Using hybrid Artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS and Neural Networks is proposed. And, instead of one solution, a preferred set of solutions near the reference points can be found. Modified Multi-objective Immune System Algorithm (MISA is proposed with real parameters value not binary coded parameters, uniform and non uniform mutation operator is applied to the clones produced. Real parameter MISA works on continuous search space.
Waiel F. Abd El-Wahed
We propose a new approach for the joint estimation of aberration parameters and unknown object from diversity images with applications in imaging systems with extended objects as astronomical ground-based observations or solar telescopes. The motivation behind our idea is to decrease the computational complexity of the conventional phase diversity (PD) algorithm and avoid the convergence to local minima due to the use of nonlinear estimation algorithms. Our approach is able to give a good starting point for an iterative algorithm or it can be used as a new wavefront estimation method. When the wavefront aberrations are small, the wavefront can be approximated with a linear term which leads to a quadratic point-spread function (PSF) in the aberration parameters. The presented approach involves recording two or more diversity images and, based on the before mentioned approximation estimates the aberration parameters and the object by solving a system of bilinear equations, which is obtained by subtracting from each diversity image the focal plane image. Moreover, using the quadratic PSFs gives improved performance to the conventional PD algorithm through the fact that the gradients of the PSFs have simple analytical formulas.
Andrei, Raluca M.; Fraanje, Rufus; Verhaegen, Michel; Korkiakoski, Visa A.; Keller, Christoph U.; Doelman, Niek
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Full Text Available In this paper we propose a novel java based computation and comparison method (JBCCM. In this method we taking three type of object oriented files for showing the computation. Those three files belong to C++, Java and C#. We first compute class, Inheritance, Interface, object and Line of Codes (LOC. Then we assume three databases based on several properties of C++, java, C#. Then we compare three files Based on class (BOC, Based on Inheritance (BOI, Based on Interfaces (BOIN, Based on Object (BOO and Based on LOC (BOL. Then we deduce a comparative result for that particular object oriented file. The result approximate that the file is best suited on which platform. So we can deduce best platform and coupling measures for Object Oriented Paradigm.
Sandeep Singh
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Full Text Available This paper deals with an approach to the optimization of enterprise information system (EIS based on the object-based knowledge mesh (OKM and binary tree. Firstly, to explore the optimization of EIS by the user's function requirements, an OKM expression representation based on the user's satisfaction and binary tree is proposed. Secondly, based on the definitions of the fuzzy function-satisfaction degree relationships on the OKM functions, the optimization model is constructed. Thirdly, the OKM multiple set operation expression is optimized by the immune genetic algorithm and binary tree, with the steps of the OKM optimization presented in detail as well. Finally, the optimization of EIS is illustrated by an example to verify the proposed approaches.
Haiwang Cao
Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking
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Full Text Available To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.
Ming Xue
Incremental structured dictionary learning for video sensor-based object tracking.
To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252
Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua
Evaluation of the reliability of transport networks based on the stochastic flow of moving objects
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In transport networks, human beings are moving objects whose moving direction is stochastic in emergency situations. Based on this idea, a new model-stochastic moving network (SMN) is proposed. It is different from binary-state networks and stochastic-flow networks. The flow of SMNs has multiple-saturated states, that correspond to different flow values in each arc. In this paper, we try to evaluate the system reliability, defined as the probability that the saturated flow of the network is not less than a given demand d. Based on this new model, we obtain the flow probability distribution of every arc by simulation. An algorithm based on the blocking cutset of the SMN is proposed to evaluate the network reliability. An example is used to show how to calculate the corresponding reliabilities for different given demands of the SMN. Simulation experiments of different size were made and the system reliability precision was calculated. The precision of simulation results also discussed
Using Rule-Based Reasoning and Object-Oriented Methodologies to Diagnose Diabetes
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Full Text Available Problem statement: Diabetes mellitus or diabetes epidemic is one of the high prevalence diseases worldwide with increased number of disability, complications and death toll. An early diagnosis helps patients and medical practitioners to reduce the burden of diabetes. Approach: In this research, we propose a framework for a system using rule-based reasoning and object-oriented methodologies to diagnose both Type 1 and Type 2 diabetes. Results: Extensive literature reviews were carried out and questionnaires were distributed to medical practitioners to build the knowledge base. This knowledge base stores the rules needed to perform a diagnosis. Conclusion: This study only presents the proposed framework and not the system itself. We believe that great improvements can be provided to the medical practitioners and also the diabetics with the implementation of this system in future.
Vithyatheri Govindan
A Software Designed For STP Data Plot and Analysis Based on Object-oriented Methodology
In the present study, we design a system that is named "STARS (Solar-Terrestrial data Analysis and Reference System)". The STARS provides a research environment that researchers can refer to and analyse a variety of data with single software. This software design is based on the OMT (Object Modeling Technique). The OMT is one of the object-oriented techniques, which has an advantage in maintenance improvement, reuse and long time development of a system. At the Center for Information Technology, Ehime University, after our designing of the STARS, we have already started implementing the STARS. The latest version of the STARS, the STARS5, was released in 2006. Any user can download the system from our WWW site (http:// www.infonet.cite.ehime-u.ac.jp/STARS). The present paper is mainly devoted to the design of a data analysis software system. Through our designing, we paid attention so that the design is flexible and applicable when other developers design software for the similar purpose. If our model is so particular only for our own purpose, it would be useless for other developers. Through our design of the domain object model, we carefully removed the parts, which depend on the system resources, e.g. hardware and software. We put the dependent parts into the application object model. In the present design, therefore, the domain object model and the utility object model are independent of computer resource. This helps anther developer to construct his/her own system based the present design. They simply modify their own application object models according to their system resource. This division of the design between dependent and independent part into three object models is one of the advantages in the OMT. If the design of software is completely done along with the OMT, implementation is rather simple and automatic: developers simply map their designs on our programs. If one creates "ganother STARS" with other programming language such as Java, the programmer simply follows the present system as long as the language is object-oriented language. Researchers would want to add their data into the STARS. In this case, they simply add their own data class in the domain object model. It is because any satellite data has properties such as time or date, which are inherited from the upper class. In this way, their effort is less than in other old methodologies. In the OMT, description format of the system is rather strictly standardized. When new developers take part in STARS project, they have only to understand each model to obtain the overview of the STARS. Then they follow this designs and documents to implement the system. The OMT makes a new comer easy to join into the project already running.
Lina, L.; Murata, K.
A Novel Multi-objective Genetic Algorithms-Based Calculation of Hill's Coefficients
The anisotropic coefficients of Hill's yield criterion are determined through a novel genetic algorithms-based multi-objective optimization approach. The classical method of determining anisotropic coefficients is sensitive to the effective plastic strain. In the present procedure, that limitation is overcome using a genetically evolved meta-model of the entire stress strain curve, obtained from uniaxial tension tests conducted in the rolling direction and transverse directions, and biaxial tension. Then, an effective strain that causes the least error in terms of two theoretically derived objective functions is chosen. The anisotropic constants evolved through genetic algorithms correlate very well with the classical results. This approach is expected to be successful for more complex constitutive equations as well.
Hariharan, Krishnaswamy; Chakraborti, Nirupam; Barlat, Frédéric; Lee, Myoung-Gyu
ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.
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This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.
Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.
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Full Text Available Feature selection (FS is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on ? - fuzzy similarity measures by multi objective genetic algorithm (FSFSM – MOGA is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it.
Hassan Nosrati Nahook,
A Moving Objects Detection Algorithm Based on Three-Frame Difference and Sparse Optical Flow
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Full Text Available Moving objects detection is an important research of computer vision. Optical flow method is a main way, but it is limited to use because of its complexity. A moving objects detection algorithm based on three-frame difference and optical flow is proposed. The calculation of optical flow is simplified. Harris corners are detected and then only the corners are selected to compute