 generating a diagnostic mask of locations of the fungal infiltrates.
Plastic-Bonded Explosives (PBXs) are a newer generation of explosive compositions developed at Los Alamos National Laboratory (LANL). Understanding the micromechanical behavior of these materials is critical. The size of the crystal particles and porosity within the PBX influences their shock sensitivity. Current methods to characterize the prominent structural characteristics include manual examination by scientists and attempts to use commercially available image processing packages. Both methods are time consuming and tedious. LANL personnel, recognizing this as a manually intensive process, have worked with the Kansas City Plant / Kirtland Operations to develop a system which utilizes image processing and pattern recognition techniques to characterize PBX material. System hardware consists of a CCD camera, zoom lens, two-dimensional, motorized stage, and coaxial, cross-polarized light. System integration of this hardware with the custom software is at the core of the machine vision system. Fundamental processing steps involve capturing images from the PBX specimen, and extraction of void, crystal, and binder regions. For crystal extraction, a Quadtree decomposition segmentation technique is employed. Benefits of this system include: (1) reduction of the overall characterization time; (2) a process which is quantifiable and repeatable; (3) utilization of personnel for intelligent review rather than manual processing; and (4) significantly enhanced characterization accuracy.
A machine capable of digitizing two 8 inch by 10 inch (203 mm by 254 mm) glass astrophotographic plates or a single 14 inch by 17 inch (356 mm by 432 mm) plate at a resolution of 11 μm per pixel or 2309 dots per inch (dpi) in 92 seconds is described. The purpose of the machine is to digitize the ~500,000 plate collection of the Harvard College Observatory in a five-year time frame. The digitization must meet the requirements for scientific work in astrometry, photometry, and archival preservation of the plates. This paper describes the requirements for and the design of the subsystems of the machine that was developed specifically for this task.
Recent developments in digital cameras in terms of an increase in size of the charged coupled device and the complementary metal oxide semiconductor arrays, as well as a reduction in costs, are leading to their use for traditional and new photogrammetric, surveying, and mapping functions. Such usage should be preceded by careful calibration of the implemented cameras in order to determine their interior orientation parameters. In addition, the wide diversity of expected users mandates the development of a convenient calibration procedure that does not require professional photogrammetrists and/or surveyors. This paper introduces a methodology for calibrating medium-format digital cameras using a test field consisting of straight lines and a few signalized point targets. A framework for the automatic extraction of the linear features and the point targets from the images, and for their incorporation into the calibration procedure, is presented and tested. In addition, the research introduces an approach for testing the camera stability, in which the degree of similarity between the bundles reconstructed from two sets of interior orientation parameters is quantitatively evaluated. Experimental results with real data proved the feasibility of the line-based self-calibration approach. In addition, the analysis of the internal characteristics of the utilized camera estimated from various calibration sessions revealed the camera's stability over a long period.
Myocardial motion analysis and quantification is of utmost importance for analyzing contractile heart abnormalities and it can be a symptom of a coronary artery disease. A fundamental problem in processing sequences of images is the computation of the optical flow, which is an approximation to the real image motion. This paper presents a new algorithm for optical flow estimation based on a spatiotemporal-frequency (STF) approach, more specifically on the computation of the Wigner-Ville distribution (WVD) and the Hough Transform (HT) of the motion sequences. The later is a well-known line and shape detection method very robust against incomplete data and noise. The rationale of using the HT in this context is because it provides a value of the displacement field from the STF representation. In addition, a probabilistic approach based on Gaussian mixtures has been implemented in order to improve the accuracy of the motion detection. Experimental results with synthetic sequences are compared against an implementation of the variational technique for local and global motion estimation, where it is shown that the results obtained here are accurate and robust to noise degradations. Real cardiac magnetic resonance images have been tested and evaluated with the current method.
The steered multiscale Hermite transform is introduced as a tool for image fusion. It is shown how this transform's particular characteristics, closely related to important visual perception properties, efficiently reproduce relevant image structures in the fused products. Two cases of remote sensing image fusion are presented, namely multispectral with panchromatic fusion and SAR with multispectral fusion. In the latter, a noise reduction algorithm also based on the Hermite transform is incorporated within the fusion scheme so that characteristic SAR image speckle is reduced and thus limited from corrupting fused products.
Registration plays a key role in multimodal data fusion to extract synergistic information from multiple non-destructive evaluation (NDE) sources. One of the common techniques for registration of point datasets is the Iterative Closest Point (ICP) Algorithm. Generally, modern day NDE techniques generate large datasets and conventional ICP algorithm requires huge amount of time to register datasets to the desired accuracy. In this paper, we present algorithms to aid in the registration of large 3D NDE data sets in less time with the required accuracy. Various methods of coarse registration of data, partial registration and data reduction are used to realize this. These techniques have been used in registration and it is shown that registration can be accomplished to the desired accuracy with more than 90% reduction in time as compared to conventional ICP algorithm. Volumes of interest (VOI) can be defined on the data sets and merged together so that only the features of interest are used in the registration. The proposed algorithm also provides capability for eliminating noise in the data sets. Registration of Computed Tomography (CT) Image data, Coordinate Measuring Machine (CMM) Inspection data and CAD model has been discussed in the present work. The algorithm is generic in nature and can be applied to any other NDE inspection data.
This paper describes tabletop display systems utilizing the stereoscopic 3D display technology. The authors have ever researched 3D display systems using the polarized glasses and liquid crystal shutter glasses, the image splitter such as a parallax barrier or the lenticular screen and the holographic optical elements1)2)3). These image splitting technologies for displaying a stereoscopic 3D image are available for developing the tabletop display that can provide different images to two users surrounding the system. To separate dual images using the polarizer slits, it is necessary to display the orthogonal polarized two images. We developed the dual LCD panel using two liquid crystal layers to make the thin and compact display system. This display panel enables observers to view full screen high resolution images. This study shows that it is possible to simplify the optical system.
The rear projection lenticular 3D display system using a projector has superior characteristics, such as having a large screen with wide field of view. However, a conventional system has disadvantages such as having images with divided horizontal resolution. We describe the 3D display system using a lenticular screen attached with vertically striped polarizer slits. This 3D display can avoid the problem of conventional system because this display shows twice the 3D image resolution. Moreover, we propose the lenticular display using double polarizer slit for elimination of pseudoscopic viewing area to solve the pseudoscopic image problem.
Finding the distance of object in a scene from vision information is an important problem in machine vision. A large number of techniques for passive ranging of unknown objects have been developed over the years (i.e. range from stereo, motion, focus and defocus). Nearly all such techniques may be framed in terms of a differential formalism. In the case of binocular stereo, two different images are taken from cameras at different discrete viewpoints, similarly, difference between consecutive images are often used to determine viewpoint derivatives for structure from motion and two or more different images taken from cameras with different aperture size are used to compute the derivative respect to aperture size for range from focus and defocus method. All this methods may be fallen into a discrete differentiation category. Farid proposed a consecutive differentiation method for range estimation which employs the intensity variation of the images along with the aperture changes to measure the range information. In this paper, we first consider the plenoptic function which is a powerful mathematical tool for understanding the primary vision problem. We then show an algorithm within a differential framework for range estimation based on the assumption of brightness constancy. Finally we show several implementations of passive ranging using this differential algorithm.
One of the main problems in visual image processing is incomplete information owing an occlusion of objects by other objects. Since correlation filters mainly use contour information of objects to carry out pattern recognition then conventional correlation filters without training often yield a poor performance to recognize partially occluded objects. Adaptive correlation filters based on synthetic discriminant functions for recognition of partially occluded objects imbedded into a cluttered background are proposed. The designed correlation filters are adaptive to an input test scene, which is constructed with fragments of the target, false objects, and background to be rejected. These filters are able to suppress sidelobes of the given background as well as false objects. The performances of the adaptive filters in real scenes are compared with those of various correlation filters in terms of discrimination capability and robustness to noise.
In pattern recognition two different tasks are distinguished; that is, detection of objects and estimation of their exact positions (localization) in images. Traditional methods for pattern recognition are based on correlation or template matching. These methods are attractive because they can be easily implemented with digital or optical processors. However, they are very sensitive to intensity degradations that always are present in observed images. In this paper we analyze and compare correlation-based methods for reliable detection