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US 20070258645 A1 Abstract Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.
Claims(20) 1. A computer-implemented method for performing recognition on an image, The computer-implemented method comprising:
identifying, from image data, an object that is posed so as to occlude one or more regions of the object; synthesizing image data for at least some of the one or more occluded regions of the object; performing recognition of the object using the synthesized image data. 2. The computer-implemented method of
3. The computer-implemented method of
4. The computer-implemented method of
5. The computer-implemented method of
6. The computer-implemented method of
7. A computer-implemented method for performing recognition on an image, The computer-implemented method comprising:
programmatically selecting, from a collection of images, a set of candidate images for a training set; presenting the set of images to a user for input; designating images from the set of candidate images for the training set based on input from the user; and using images from the training set to perform a recognition of one or more images. 8. The computer-implemented method of
9. The computer-implemented method of
10. The computer-implemented method of
11. The computer-implemented method of claim 1O, wherein using images from the training set to perform a recognition includes presenting to the user a set of recognized images that each include a recognized object or face that is contained in each image of the training set, and wherein the method further comprises enabling the user to identify if any of the images in the set of recognized images includes an erroneous recognition.
12. The computer-implemented method of
13. The computer-implemented method of
14. The computer-implemented method of
15. A computer-implemented method for performing recognition on an image, method comprising:
determining a region of an image where an object that is to be recognized is likely located; for each of a plurality of features of the object that is to be recognized, determining a set of one or more hypotheses regions of the image that are likely locations of that feature in the object that is to be recognized. determining a most likely location of at least some of the plurality of features using the set of one or more hypothesis regions for all of the plurality of features. 16. The method of
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Description This application claims benefit of priority to U.S. Provisional Patent Application No. 60/781,691, filed Mar. 12, 2006, entitled �Methods for Registration of Faces for Face Recognition�; the aforementioned priority application being hereby incorporated by reference in its entirety. This application also claims benefit of priority to U.S. Provisional Patent Application No. 60/781,690, filed Mar. 12, 2006, entitled �Use of 3D Models for Face Recognition�; the aforementioned priority application being hereby incorporated by reference in its entirety. This application also claims benefit of priority to U.S. Provisional Patent Application No. 60/781,966, filed Mar. 12, 2006, entitled �Algorithms to Intelligently Choose Labeling Examples for Face Recognition and Dynamically Changing the Acceptance Thresholds for Face Recognition�; the aforementioned priority application being hereby incorporated by reference in its entirety The disclosed embodiments relate generally to the field of digital image processing. More particularly, the disclosed embodiments relate to a system and method for enabling the use of captured images. Digital photography has become a consumer application of great significance. It has afforded individuals convenience in capturing and sharing digital images. Devices that capture digital images have become low-cost, and the ability to send pictures from one location to the other has been one of the driving forces in the drive for more network bandwidth. Due to the relative low cost of memory and the availability of devices and platforms from which digital images can be viewed, the average consumer maintains most digital images on computer-readable mediums, such as hard drives, CD-Roms, and flash memory. The use of file folders are the primary source of organization, although applications have been created to aid users in organizing and viewing digital images. Some search engines, such as GOOGLE, also enables users to search for images, primarily by matching text-based search input to text metadata or content associated with images. Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people. When employed on a collection of digital images, embodiments described herein enable the use of several preliminary steps to enhance the performance of recognition and the results derived therein. These preliminary steps may include (i) establishing a labeled training set of images from which subsequent recognition can take place; (ii) registering faces or objects, to facilitate the ability of a recognition algorithm to be able to determine corresponding points on different objects or faces; and (iii) enabling the recognition algorithms to compensate or otherwise overcome occlusion or posing of the object that would otherwise diminish or preclude the algorithm from detecting features or corresponding points of the object of interest. Embodiments described herein provide for computer-implemented image recognition of objects in images. According to an embodiment, an object is identified from an image, where the object that is posed so as to occlude one or more regions of the object in the image. Image data is synthesized for at least some of the one or more occluded regions of the object. Recognition is performed on the object using the synthesized image data. According to another embodiment, recognition is facilitated or enabled through the creation of a training set, using a programmatic and adaptive technique for identifying or selecting images for use in a training set. In one embodiment, a set of candidate images are programmatically selected, from a collection of images, for a training set. The set of images are then presented to the user. Individual images from the candidate set are presented to the user for feedback or input, and select images presented to the user may be identified as part of the training set. According to an embodiment such as described, the training set may continuously be modified or updated. As an addition or alternative, a threshold criteria for selecting images in the training set may be made adaptive based on factors that include the error rate or other measure of how well recognition is performed with the given training set. Examples of objects that may be used with one or more embodiments include a face, a person, apparel, clothing, merchandise, and animals. According to another embodiment, image recognition includes determining a region of an image where an object that is to be recognized is likely located. The object of interest may be known to have specific set of features, which may be defined in variety of ways (e.g. geometric constraints) through two or three dimensional models. For each of a plurality of features of the object of interest, a set of hypotheses regions of the image may be determined. These hypotheses regions may correspond to likely locations of that feature in the object that is to be recognized. A most likely location of at least some of the plurality of features using may then be determined using the set of one or more hypothesis regions for all of the plurality of features. Various applications and implementations are contemplated for one or more embodiments of the invention. As used herein, the term �image data� is intended to mean data that corresponds to or is based on discrete portions of a captured image. For example, with digital images, such as those provided in a JPEG format, the image data may correspond to data or information about pixels that form the image, or data or information determined from pixels of the image. The terms �recognize�, or �recognition�, or variants thereof, in the context of an image or image data (e.g. �recognize an image�) is meant to means that a determination is made as to what the image correlates to, represents, identifies, means, and/or a context provided by the image. Recognition does not mean a determination of identity by name, unless stated so expressly, as name identification may require an additional step of correlation. As used herein, the terms �programmatic�, �programmatically� or variations thereof mean through execution of code, programming or other logic. A programmatic action may be performed with software, firmware or hardware, and generally without user-intervention, albeit not necessarily automatically, as the action may be manually triggered. One or more embodiments described herein may be implemented using programmatic elements, often referred to as modules or components, although other names may be used. Such programmatic elements may include a program, a subroutine, a portion of a program, or a software component or a hardware component capable of performing one or more stated tasks or functions. As used herein, a module or component, can exist on a hardware component independently of other modules/components or a module/component can be a shared element or process of other modules/components, programs or machines. A module or component may reside on one machine, such as on a client or on a server, or a module/component may be distributed amongst multiple machines, such as on multiple clients or server machines. Any system described may be implemented in whole or in part on a server, or as part of a network service. Alternatively, a system such as described herein may be implemented on a local computer or terminal, in whole or in part. In either case, implementation of system provided for in this application may require use of memory, processors and network resources (including data ports, and signal lines (optical, electrical etc.), unless stated otherwise. Embodiments described herein generally require the use of computers, including processing and memory resources. For example, systems described herein may be implemented on a server or network service. Such servers may connect and be used by users over networks such as the Internet, or by a combination of networks, such as cellular networks and the Internet. Alternatively, one or more embodiments described herein may be implemented locally, in whole or in part, on computing machines such as desktops, cellular phones, personal digital assistances or laptop computers. Thus, memory, processing and network resources may all be used in connection with the establishment, use or performance of any embodiment described herein (including with the performance of any method or with the implementation of any system). Furthermore, one or more embodiments described herein may be implemented through the use of instructions that are executable by one or more processors. These instructions may be carried on a computer-readable medium. Machines shown in figures below provide examples of processing resources and computer-readable mediums on which instructions for implementing embodiments of the invention can be carried and/or executed. In particular, the numerous machines shown with embodiments of the invention include processor(s) and various forms of memory for holding data and instructions. Examples of computer-readable mediums include permanent memory storage devices, such as hard drives on personal computers or servers. Other examples of computer storage mediums include portable storage units, such as CD or DVD units, flash memory (such as carried on many cell phones and personal digital assistants (PDAs)), and magnetic memory. Computers, terminals, network enabled devices (e.g. mobile devices such as cell phones) are all examples of machines and devices that utilize processors, memory, and instructions stored on computer-readable mediums. Recognition of Occluded Portions of an Object in an Image In the field of digital photography, objects in images are often posed at an angle or tilt with respect to the viewpoint of the camera lens. The ability of image recognition systems or software to work properly on such objects may be negatively affected. For example, an object at a tilt or angle may have portions of the object occluded or otherwise not visible in the image. Embodiments described herein accommodate or otherwise compensate for occurrences of tilts in the pose of objects, including in instances when occlusion is present in the object of interest, when recognition is performed on posed objects of images. Facial recognition is an example of one type of recognition performed with digitally captured images. In general, facial recognition identifies the location of a face of a person in an image, then seeks to use a signature of the person's face to identify that person by name or by association with other images that contain that person. Non-facial recognition may be similarly performed, in that a pre-determined object may be detected from a digital image, then a signature of the object may be used to identify the object by class or otherwise. One or more embodiments recognize that for image recognition to be performed accurately on, for example, consumer images or randomly posed objects in images, either (i) the face/object being recognized can be assumed to be frontally aligned, or (ii) occlusion or pose distortions of the object of interest must be accommodated or compensated for programmatically or otherwise. For example, in the absence of occlusion compensation, facial or object image recognition may require utilization of simplifying assumptions about the face or object being recognized. Such assumptions may assume, for example, the face or object is in a frontal position (relative to the camera) when the image is captured. In practice, large variations in viewpoints are extremely common in consumer photographs. With regard to facial recognition, in particular, head rotations can produce significant changes and distortions in the facial appearance of an individual in a photograph. Such effects generally cause significant degradation of face recognition performance. Embodiments described herein enable generation of a realistic image of a face or other object, using as input, an original image that has one or more portions of the face or object occluded or rotated. In particular, one or more embodiments provide that a synthesized image may be generated of a face or object. The synthesized image may present the face or object in a frontal view and at a specified position or viewpoint. The input for creating the synthesized image may correspond to an image that contains the face or object posed at some arbitrary viewpoint that causes occlusion. In one embodiment, a synthesized frontal view of a face can replace the original photograph as input in a face recognition system in order to produce more accurate recognition performance. Alternatively, a synthesized frontal view of an object can replace the original photograph as input in an object recognition system. For example, the object may correspond to clothes, apparel, merchandise, a person (rather than just a face) or even an animal or animal face. In another embodiment, a combination of the two-dimensional original view and a synthesized frontal view can be used at the same time. For this task, while matching two faces, either a weighted average of the two, or the minimum distance of the two metrics can be used. One or more embodiments described herein recognize that a frontal view of an object may be synthesized from a non-frontally viewed image of that same object using comparison or prior knowledge of a generic appearance of the object or of a class or kind of the object. In one embodiment, a frontal view of a face may be synthesized from a non-frontally viewed image of that face using prior knowledge of a generic appearance of a face (or a type of face) or an object. According to one embodiment, a geometric three-dimensional model of an object (including face) that is to be recognized is used to provide prior knowledge or reference for synthesis. System 100 may receive image data 112 from an image source 102. Examples of the image source 102 include a digitally captured image, such as images captured by digital cameras or images presented with other content. The resolution of the image source 102 may vary, depending on, for example, an application of an embodiment of The object detector 110 may scan image data 112, corresponding to the image file 102, to detect an object of interest. In one embodiment, the object of interest corresponds to a face of a person. Detection of the face may be performed by scanning the image data 112 for features that are indicative of a face, such as the eyes, eyebrows, or corners of a mouth. Other examples of objects of interest include clothes, apparel, merchandise, a person (rather than just a face), an animal or animal face. For each type of object that is of interest for recognition, markers of that object's class may be pre-defined, and images may be scanned for such markers in order to detect presence of those objects in an image. Object image data 122 may be used by the pose/occlusion module 120 to determine whether the detected image is posed to a degree that would cause occlusion or other significant deterioration of recognition. Alternatively, the pose/occlusion module 120 may detect, from the object image data 122, whether the image is posed to a degree that would hinder or interfere image recognition analysis on the object. The pose/occlusion module 120 may determine that the pose of the object of interest within an acceptable threshold, in which case no synthesis is performed. If, however, the pose/occlusion module 120 determines that the pose of the object of interest is significant, or if there is occlusion from the pose of the object of interest, the pose/occlusion module 120 executes to determine augmentation information 132. In one embodiment, augmentation information 132 may utilize a three-dimensional model of a generic or non-specific object (of the same class as the object of interest) to quantify the pose (e.g. orientation) and position of the object of interest, as well as the intensity of discrete points of the object of interest. The synthesizer 130 may utilize the object image data 122, as well as the augmentation information 132 as determined from the pose/occlusion module 120, to generate a synthesized image 142 of the image file 102. The synthesized image 142 may use the augmentation information 132 to re-orient and possibly re-position the object of interest. For example, if the object of interest is a face that is tilted one way or another, the synthesized image 142 may re-align the face so as to orient it towards a hypothetical camera angle. In an embodiment, synthesized image 142 is simply an output of system 100. For example, images in a photo-library may be scanned, and faces of persons appearing at an angle may be synthesized and presented as an independent set of images, each with the images of the person provided frontally. In another embodiment, synthesized image 142 is generated for purpose of performing a process such as image recognition. In cases when the object of interest, as determined from the image file 102 is too angled and/or occluded to perform recognition, synthesized image 142 may be generated as replacement input. Image recognition module 140 may perform recognition on the object of interest using the synthesized image 142 as input. One or more embodiments described herein recognize that a frontal view of an object may be synthesized from a non-frontally viewed image of that same object using comparison or prior knowledge of a generic appearance of the object or of a class or kind of the object. Step 210 provides that a geometric three-dimensional model of an object that is to be recognized is formulated. The model may be generic to a class of the object. For example, in face recognition, the model may be for a non-specific or generic face, having characteristics or features generally known to be part of faces. The model may correspond to, or form the basis of, a reference or prior knowledge for when image synthesis is to occur. In one implementation, a suitable model can be a triangular mesh of three-dimensional points, specified by a set of vertices and edges. Alternatively, the three-dimensional model may be in the form of a discrete cloud of three-dimensional points, from which a continuous three-dimensional surface may be computed by standard methods of interpolation and extrapolation. In yet another implementation, the model may be in the form of a deformable graph of connected three-dimensional points that can be perturbed in three-dimensional space in some specified way in order to account for geometric facial variations among individuals or to represent facial expressions, such as smiling or frowning. In step 220, the image that is to undergo recognition is analyzed to determine pose and position of the object of interest. Given a non-frontal face photograph, one embodiment provides a computer-implemented algorithm that analyzes the image in order to recover information indicating a rotation or position of the object in the image relative to the camera. For example, with regard to facial recognition, such an algorithm may first seek to recover the rotation and position of the face three-dimensionally, from the perspective of the camera lens. This can be formulated as the task of �virtually� orienting and positioning the three-dimensional model in space in such a way that the simulated projection of rays from the model to the camera best match the face in the photograph. For example, the ray departing from the tip of the nose of the three-dimensional model may intersect the camera image plane at the tip of nose of the face in the photograph. The relationship between the points of the three-dimensional model in space and the image points of the face in the photograph can be described by a mathematical camera model, such as orthographic or full perspective projection. According to one embodiment, sub-step 224 (of step 220) provides that attributes of position and orientation may be determined from use of projective three-dimensional models. In one embodiment, an unknown orientation and position of the object (such as a face) in three-dimensional space can be specified in terms of a rotation matrix, and a translation vector. These unknown parameters can be computed by establishing a set of correspondences between the three-dimensional model points and the image points. For example, an image registration algorithm (such as described with one or more embodiments of Step 230 provides that the three-dimensional model (established in step 210) is augmented using one or more attributes from the image. In one embodiment, the attribute used to augment the three-dimensional model is intensity. As illustrated by sub-step 234, once the three-dimensional orientation and position of the face has been recovered, an intensity value is associated to each point of the three-dimensional model. The intensity value for each three-dimensional point is set to be the image value at the projection of the three-dimensional point onto the photograph, according to the recovered three-dimensional alignment and camera model. This process augments the three-dimensional model with the description of the appearance of each model point in that photograph. Some of the three-dimensional points of the model might not be visible in the photograph due to occlusion caused by other parts of the face. The system can detect which points are not visible by using methods of surface-ray intersection computation. In one embodiment, the intensities of occluded three-dimensional points can be filled-in by exploiting face symmetry: for example, the system might set the image intensities of the left side of the nose to be identical to those of the right side, if the left side is detected to be occluded. As an alternative to face symmetry, one or more embodiments provide that intensities of occluded regions can be marked, such as with text or a flag. For example, such portions may optionally be marked as �not available�. In step 240, the three-dimensional model augmented with intensity values is used to synthesize the frontal view of the face. The three-dimensional model is first aligned to be in exact frontal position and then standard rendering methods, such as ray casting, are used to generate the new photograph. According to an embodiment, if there are any occluded regions that are marked as �not available�, the corresponding pixels are left unfilled in the final photograph. Programmatic and Intelligent Selection of Images for Training Sets and Labeling In order to perform recognition of a desired object, image recognition techniques sometimes require a training set. The training set provides identified faces (or other objects) that, as a point of comparison, provide identity information when recognition is performed on a set of images undergoing recognition. If an object in an image undergoing recognition is deemed, through recognition, to be the same object (or alternatively the same class of object) as one of the objects in the training set, then the identity of the object under recognition is determined to be the same as that identity of the corresponding object of the training set. In the case of facial recognition, for example, the training set may include images of faces of different persons. A set of images undergoing recognition may be compared against the training set, and if matches are found between the two sets, then the identity of a recognized face from the second set is assigned the identity of the matching face from the training set. In order to provide identity information, objects contained in images of the training set may contain one or more signatures or other data identification, as well as a label or tag provided by a user. For facial recognition, the label or tag may correspond to a name. Embodiments described herein enable a user to scan a set of digital images in order to assign labels (e.g. corresponding identity information) to object or faces in the images. However, embodiments described herein also recognize that the user may not be the best source for identifying the training set from a library of images. In many cases, users typically are unaware of what images in their data set or suited for training set. For example, the user may identity images having faces or objects that are difficult to recognize because, for example, the object is severely tilted in its pose or positioned poorly for recognition in the image. One or more embodiments provide for an algorithm that can be executed through use of a computer in order to programmatically select examples of images that can provide an adequate reference for inclusion in a training set. In one embodiment, an image for the training set may be programmatically selected by selecting the image of an object or face that produces, from within a library of collection of images, the maximum recognized number of objects when used as part of training set by a face recognition algorithm. The programmatically selected images (or the portions containing the object that is to be recognized) may be displayed or otherwise rendered to the user. The user is then able to label the pictures, and the labeled pictures may be used as a training set by the image recognition program. An approach as described with such an embodiment provides several benefits. The user is required to know information for how to select images for a training set. An embodiment such as described may also optimize images that are returned and used for a training set, thus providing some limit to the amount of labeling that may otherwise be required of the user. Moreover, the quality of the training set is high. In a step 310, a set of candidate images are selected from a collection. Numerous techniques may be employed in order to determine an initial set of candidate images for a training set. The algorithm for choosing the training examples can be designed in many ways. In one embodiment, any clustering algorithm can be used, such as k-means clustering. The clustering algorithm would segment the group of faces into groups (clusters) based on a vector description (feature vector) for each face. Once the clusters are obtained, different metrics are obtained for each cluster. These different metrics include, for example, cluster size and/or cluster compactness. Cluster size determines how many objects or faces are included in that particular cluster. Cluster compactness determines how spread the faces in that cluster are. The more spread is the cluster, the more likely that there would be errors (multiple identities) within that cluster. Metrics such as cluster size and compactness may be used to choose cluster centers to be presented to the user. The first metric optimizes the number of recognized faces, whereas the second metric optimizes the accuracy of the recognition. A combination of these metrics can also be used. In step 320, the candidate images are presented to the user for labeling or other input. For example, for a given collection, a set of images may be displayed to the user, where different faces or objects of interest are identified from the images in the set. According to one implementation, presenting the set of candidate images to the user is performed with chosen cluster centers (corresponding to the face or object closest to the cluster center). In step 330, user-input is received. The user-input corresponds to, for example, labeling. In facial recognition, for example, labeling may correspond to the user providing a name, nickname or other individual or class identifier to a picture of a face or person. In step 340, the training set is established using the candidate images and the input or labels provided from the user. The training set is then subsequently used to recognize select or desired objects from images that undergo recognition. With regard to an embodiment of As an alternative to an embodiment that uses clustering, another embodiment provides that step 310 may be performed, at least in part, by an algorithm that simulates face or object recognition. Such an algorithm may speculate different training sets, and then would programmatically select the training set that returns the maximum recognized faces or objects. In one embodiment, such an algorithm may be determined by the following sub-steps:
(iv) calculating the optimization function for every face i. A sorted index is obtained by sorting these function values. This sorted list is then traversed while a list of chosen labels and potentially recognized faces are constructed. The chosen labels list is the final list to be shown to the user, while the potentially recognized face list is the list of faces estimated to be recognized given the selection of chosen labels. A pseudo code for this step is provided below.
As described with one or more embodiments described above, labels may subsequently be obtained from the user. Object recognition systems and techniques (including general pattern recognition) may be implemented in a supervised or semi-supervised learning environment. Embodiments described herein further provide an adaptive acceptance threshold which controls the number of errors made by the system during a learning process, based on the historic performance on that task, as well as the expected difficulty of the task. In a step 410, a training set is formed. One or more embodiments provide that a training set may be formed using, for example, an embodiment such as described with Step 420 provides that recognition is performed for individual images in a collection. The recognition may be for objects and/or faces. The recognition may be performed by comparing objects of interest from images in a collection with corresponding images from the training set. In performing recognition of step 420, a sub-step 424 provides that a programmatic determination is made of individual images in the collection that contain an object of interest, with confidence values that within a designated threshold. The confidence values provide a quantitative measure as to a level of certainty that a recognition was correctly performed. Thus, for example, a program may scan and analyze a collection of digital images, and determine recognition for one or more of the images, provided the confidence values of the recognition are within a designated threshold or tolerance level. In a step 430, image results from performing a recognition process on individual images contained in the collection are presented to the user for verification and feedback. In particular, an embodiment provides that the user may mark individual recognition results as correct or incorrect. Correct and incorrect results may be tabulated or otherwise tracked. Step 440 provides that an error rate is determined from the user verification. In step 450, the confidence value threshold for determining recognition of objects in images is adjusted upward or downward, based on the error rate. For example, if the error rate is determined to be high (based on internal standard), the threshold may be lowered to reduce the error rate. As an alternative or addition to adjusting the threshold, correctly recognized results may be added to the training set, so that the training set is progressively increased in size. Likewise, an error set may be maintained and updated for incorrect results. One or more both of the progressively updated training set and error set may be used to increase the recognition rate. The following provides an example of how an embodiment such as described with According to one embodiment, correct results may be added to the training set. Incorrect results may be added to a set of error examples. The set of error examples is used in turn along with the training set and potentially additional user supplied examples to run recognition again and increase the number of recognized faces. This process can go on for multiple iterations as long as the user supplies additional information to the recognition software. According to one or more embodiments, a user verification process is often much more labor intensive when the user is presented with a lot of errors. To maximize user satisfaction, the following parameters may be controlled: (i) the total number of errors #E presented to the user; and (ii) the relative error rate, defined as #E/#R, where #R is the number of results. At the same time, the total number of correct faces recognized should not be impacted. Controlling the number of errors and the error rate can be achieved by altering the acceptance threshold: a higher threshold will reduce the number of errors, at the cost of getting fewer recognition results. A lower threshold will cause the system to accept more recognition results, at the risk of letting through more unwanted errors. One or more embodiments (such as described with an embodiment of Since the number of errors #E made by the system is unknown at the stage where the threshold determination is made, and then only learned subsequently, one or more embodiments uses an estimate of #E to determine the designated threshold on-the-fly. Such a determination may be based on multiple sources of information, including but not limited to:
More specifically, the decision threshold T can be written as: T=TO+f(#R, #E, #U), where TO is a default threshold and f() a function which maps the numbers collected from the data to a confidence penalty. In a more specific instance, the expected error rate can be based on a smoothed estimate of the error rate so far: #EE=#E/(#R +a), �a� being a constant. The expected number of faces to be recognized can be based on a fraction of the total number of unknown faces: #UUI=min(1, #U/b), �b� being a constant. The function f can then be a function of the two parameters: f(#EE,#UU). As an example, f can increase with the proportional number of errors (#EE), but decreases with reduced number of remaining unknown faces (#UU). A graph of The curves with �+� markers use a fixed threshold T0, and the curves with �.� markers use the adaptive threshold T described in this invention. The dynamic threshold contributes to achieving various benefits, such as (i) not to reduce the number of correct faces recognized, (ii) to control the number of errors made by the system (X axis), (iii) to control the relative error rate of the system. In a graph shown, a relative error rate is the slope of the curve. As an illustration, the black diagonal line has a slope corresponding to a 10% relative error rate. Registration As mentioned, an image recognition system (such as for faces and objects) can analyze two images and determine if the same subject (or class of object) is present in both. To do this, regions around corresponding points on the object of interest need to be compared. However, finding these corresponding points is a challenging task in digital images (such as consumer photographs), which contain large appearance variability amongst faces or objects due to changes of viewpoint, object deformations (such as expression on a face) and lighting. One or more embodiments provide a registration algorithm, which can find corresponding object features in the presence of such appearance variability, as an integral part of an overall recognition system for use in digital images. Embodiments described herein include a system for finding corresponding points in objects of interest that appear in, for example, consumer photographs. When applied to face registration, for example, such a process may be referred to asface registration. Given an image of a face, the algorithm determines the precise locations of a set of multiple features in the image, which include but are not limited to the eyes, the tip of the nose, the ends of the mouth, and the locations of the ears, hairline, and eyebrows. Similar processes may be performed with other kinds of objects. For simplicity and illustrative purposes, however, some embodiments described herein may refer to techniques that use face registration, and it is understood that face registration is representative of recognition of other objects. In another embodiment, the locations of these marker features are used to compute a dense correspondence map of between all pixels in the image and the pixels of a generic face model, represented as a two-dimensional image. In another embodiment, the dense correspondence map can be computed between the image pixels and the vertices of a generic three-dimensional object or face model. According to an embodiment, an algorithm for object or face registration may be configured to locate a location of multiple marker features in a photograph. As mentioned, a photograph containing a face, for example, may have marker features corresponding to the eyes, mouth, nose etc but also any other parts of the face which may not correspond to a specific anatomic part. As mentioned with one or more embodiments, an object of interest may correspond to a face or some other object. Accordingly, one or more embodiments define a probabilistic model in the form of a Markov network, which combines information about local appearance of the features in images, as well as geometric constraints on the relative location of the features. A Markov network associates a location variable Xi with each face feature Fi. The domain of the variable is discrete, corresponding to several distinct location hypotheses for that feature in the image. Afeature appearance potentials may be defined as
In one embodiment, a local feature appearance model may be used. Such a model may be learned from ground truth data, which contains example images of the objects of interest, or faces, where the locations of the features of interest are human-labeled. In one embodiment, a Gaussian appearance model is assumed for the pixel intensities of an image patch centered on a particular feature. A low-dimensional feature space is estimated by using Principal Component Analysis (PCA) and the largest principal components are retained. The local appearance potential φ(Xi=[OOx,y]) assigns a high likelihood in cases when (i) The difference between the image patch centered on [OOx,y] and its PCA space reconstruction is small, and (ii) The Mahalanobis distance between the mean feature appearance and the image patch projection to PCA space is small. The overall likelihood is determined as a weighted combination between the above two criteria, where the tradeoff is estimated by cross-validation on the ground-truth data. In another embodiment, a local feature appearance model can be learnt using classifiers such as boosting (Adaboost) or support vector machines (SVM). In this formulation, the ground truth data for each feature contains a set of positive and negative examples, and the goal is to classify the patch into one containing the feature at its center or not. A trained classifier (Adaboost or SVM) not only classifies a given patch but also provides a certainty score. This certainty score can be used to obtain the values of the local appearance potential φ(Xi) as follows. A subset of the ground truth data (a holdover set) is left out from the examples provided to the classifier. The learned classifier is applied to this subset of examples and for each certainty score of the classifier the probability that the classifier is correct is estimated. To avoid overfitting, the ratio can be computed for certainty score ranges. Alternatively, a logistic regression model can be used. The resulting probabilities are used as values in the local appearance potential φ(Xi). The probabilistic model also enforces constraints on the relative location of the face features. This is done with pairwise potentials φ(Xi, Xj) although higher-order potentials can be used as well. Such pairwise potentials assign a probability for each possible combination of the locations of Xi and Xj, expressing preferences such as �the right eye is placed to the right of the left eye�. They can be introduced between all pairs of features (O(N2) potentials for N features) or between each feature a few of its nearest neighbor features (complexity O(kN) if k neighbors are used). The values of these features are estimated from data as follows. The oriented vector d=Xj−Xi for each considered pair of features is computed in all labeled training examples, and a Gaussian distribution is learnt over the vectors dij for each pairwise potential φ(Xi, Xj). The feature appearance and relative position in images changes for different orientations of the object of interest. Therefore, one or more embodiments recognize that it may be advantageous to group the ground truth examples by face pose, and learn separate feature appearance and relative feature location models for each of these poses. In another embodiment, when the appearance models are not substantially different, the same appearance model can be used for several features and/or in several poses (for example, the same appearance model can be used for right eye and left eye). In step 620, a set of location hypotheses for each object feature or marker may be determined. In one implementation, a Markov network may be constructed to contain single and pairwise potentials, φ(Xi) and φ(Xi, Xj), as described elsewhere in this application. Step 630 provides that a most likely hypothesis location is determined from the set for each of the markers or features of the object of interest. In one implementation, for example, a Loopy Belief Propagation may be used to obtain the most likely feature locations that maximize the probability defined by the Markov network. Because different Markov network models have been learned for each different face pose, in principle, an algorithm may be implemented to try all these models and select the model and solution, which provides the highest joint likelihood. For efficiency, one or more embodiments incorporate a pose classifier to predict likely face or object poses. The registration module then may use Markov network inference only for those poses. The pose classifier can be trained using Adaboost, or can use separate Gaussian face appearance models for each pose, similar to the local feature appearance models described elsewhere in this application. The feature or marker locations of the face or object of interest, obtained as described with an embodiment of In another embodiment, the feature locations can be used to compute a dense correspondence map between the face image and a generic three-dimensional face model. Again, images mapped to the same three-dimensional face model can be easily compared. The registration module 720 performs a registration process or algorithm on the region of the image where the object of interest is detected, as provided by detected object image data 712. The registration module 720 may implement one or more algorithms for performing registration, including, for example, an algorithm in accordance with an embodiment of If the object is posed at a tilt that makes complete recognition difficult or impossible, the synthesizer module 730 may be used to generate or synthesize an image, provided by synthesized image data 732. The synthesized image data 732 may accommodate or compensate for presence of occlusion or distortion, as a result of the tilt in the object of interest in the image file 708. As an alternative or addition, the image may be normalized to facilitate the recognition. as described with an embodiment of The recognition module 750 may use synthesized (for tilted posed objects) and non-synthesized images (when appropriate or possible) or a combination of the two to perform recognition. A reference or training set 742 may be established for use by the recognition module 750. Training module 740 may be configured, for example, in accordance with an embodiment of Moreover, as described above, one or more embodiments provide that the training set 742 created by the training module 740 becomes progressively larger based on an error rate, further increasing the recognition module's performance. The use of synthesizer module 730, to provide synthesized image data 732 when necessary, also enhances performance of the system as a whole, as recognition can be carried out to objects with tilts and poses that would otherwise be too severe for regular recognition. In an embodiment, computer 800 may be part of a server system that provides a web service. For example, computer 800 may store image files from users that communicate with the computer using an Internet connection and web browser. In one implementation, computer 800 performs acts of recognition and rendering of results, as well as display of user-interfaces for receiving information such as labeling and responses to programmatic training set determinations. As mentioned, it is contemplated for embodiments of the invention to extend to individual elements and concepts described herein, independently of other concepts, ideas or system, as well as for embodiments to include combinations of elements recited anywhere in this application. Although illustrative embodiments of the invention have been described in detail herein with reference to the accompanying drawings, it is to be understood that the invention is not limited to those precise embodiments. As such, many modifications and variations will be apparent to practitioners skilled in this art. Accordingly, it is intended that the scope of the invention be defined by the following claims and their equivalents. Furthermore, it is contemplated that a particular feature described either individually or as part of an embodiment can be combined with other individually described features, or parts of other embodiments, even if the other features and embodiments make no mentioned of the particular feature. This, the absence of describing combinations should not preclude the inventor from claiming rights to such combinations. Patent Citations
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text:
- Shopping Bag ( 0 items )
Brian Hester
Suspenseful, Intrigue, Intelligent and Action PackedI look forward to reading Clive Cussler novels and look for his new releases with anticipation. This book with Jack Du Brul is great. I have followed Juan Cabillos exploits on many missions and always wonder how they can get any better. The Jungle was terrific and kept me guessing. I looked forward to opening up my electronic reader and diving back into the story, just to see where I could go next with Juan and his crew . OUTSTANDING READ

subset: commoncrawl


text:
Easy Homemade Plastic Bottle Mouse Trap Catch mice in your home with a water bottle! 2/3/2015 12:41:21 PM 8,533 74
Face Rocket A chick decides to light a decent sized bottle rocket from her mouth. 11/7/2008 12:00:00 AM 62,930 43
Failed Bottle Smash Another drunk person trying to smash a bottle of their head. 6/12/2007 12:00:00 AM 114,768 34
Fastest Way To Drink Bottled Water Damn that was super fast. Now do a beer! 1/7/2013 12:00:00 AM 26,157 70
Fishing With Bottles This is pretty wild. Its called jugging. Look at the fish some chick... 8/15/2007 12:00:00 AM 186,858 72
Frozen Soda Explodes In Kids Hand This kid was caught off guard by the size of the explosion. 7/21/2013 12:43:29 PM 21,626 42
GoPro Attached To Fireball Whiskey Bottle at Wedding Another creative way to use your GoPro! 10/27/2014 12:16:38 PM 10,126 28
Guy Accidently Smashes Case of Beer He Just Bought This idiot attempts to ride his shopping cart with freshly bought ice cold... 5/27/2014 11:33:35 AM 14,336 57
Guy Chugs a Bottle of Maple Syrup He died of diabetes later that day... 1/15/2013 12:00:00 AM 13,032 34
Guy Chugs Bottle of Vanilla Vodka in 22 Seconds Anyone want to donate a liver to this guy? 5/7/2013 11:06:36 AM 19,372 58


text:
Former Heavyweight Champion Mike Tyson Dead at 46
Former Heavyweight Champion Mike Tyson Dead at 46.
Had Fox News Call Messi Died
AFP – Barcelona, World soccer was created by the media commotion from the United States, Fox News, when Fox called the Barcelona star, Lionel Messi, died. But the news was false and was clarified by Fox.
Junior Saeu's Questionable Death
People are still wondering why Junior Seau an ex football playertook his life. Seau had a positive personality that reflected on others.
UFC Fighter Kimbo Slice Dead at 36
Former Street Fighter and UFC fighter Kevin Ferguson, also known by his fighting name Kimbo Slice died late Saturday evening from various injuries suffered in his UFC loss to NFL veteran Matt Mitrione.


text:
Crisp-topped sprouts
By Emma Lewis
Cooking time
Prep: 15 mins Cook: 15 minsSkill level
Spruce up your usual sprouts and add crunch and crispiness with a bread and almond-based topping
Nutrition and extra info
Additional info
- Easily halved
- Vegetarian
Nutrition info
Nutrition per serving
- kcalories
- 125
- protein
- 6g
- carbs
- 9g
- fat
- 8g
- saturates
- 1g
- fibre
- 6g
- sugar
- 4g
- salt
- 0.11g
- 1kg Brussels sprouts
- 50g white bread, preferably ciabatta
- 2 tbsp olive oil, plus extra to serve
- 25g flaked almonds
- 1 garlic clove, finely chopped
- zest 1 lemon
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- Remove any tough leaves and trim sprouts, then steam or boil for 10 mins until tender.
- Tear the bread into crumbs. Heat a large frying pan and pour in the olive oil. Add the bread and fry until just crisp. Tip in the flaked almonds, garlic and lemon zest, then cook gently until everything is golden. Place the sprouts in a serving dish, season, then toss with the crumbs and a little extra olive oil to finish.
Recipe from Good Food magazine, December 2007
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text:
Football in Malaysia
Football is unquestionably the number one sport in Malaysia, a country that has a long history of international footballing activity: qualification for the 1972 Olympic Games in Munich; third place at the final competition of the 1974 Asian Cup of Nations in Iran; winners of the Merdeka tournament in 1974, defeating Korea Republic in the final; winners of the Merdeka tournament in 1976, overcoming Japan in the final. Malaysia also qualified for the 1980 Olympic Football Tournament in Moscow, and as the host nation of the 1997 FIFA World Youth Championship, the Malaysian youngsters also graced the tournament. Players such as Dato' M. Chandran (1972 Olympics) and Dato' Soh Chin Aun (1980 Olympics), among others, with Zainal Abidin Hassan perhaps the most famous name in Europe, were the stars of Malaysian football's finest hours.
The first Goal Project
Approved by the Goal Bureau on 17 February 2004, Malaysia's project focuses upon the laying of a floodlit artificial pitch within the grounds of the association's football academy in Kuala Lumpur. These facilities will provide a much-needed boost, mainly to young players who will be able to play football all-year round as well as after sunset. Funded by donations from Goal, the FIFA Financial Assistance Programme and the asso-ciation's own resources, the complex was opened by FIFA President Joseph S. Blatter on 6 May 2007.
Use of FAP funds
Financing of Goal project
Floodlit artificial pitch in Kuala Lumpur
Project approved
17 February 2004
Opened on 6 May 2007
Financed by
Goal USD 400,000 FAP USD 247,884 Association USD 300 000 Total cost USD 947,884


text:
Installation Magazine
By Installation Media
Open iTunes to buy and download apps.
The First All-Digital Contemporary Art Magazine... Welcome to INSTALLATION MAGAZINE, the most interactive and engaging art magazine available on your iPad, iPhone and iPod touch. This contemporary arts magazine is packed with international artists, entertainment, design, fashion, literature and the most exciting features on private collections.
Rave reviews:
"New and Noteworthy" - iTunes App Store
"Staff Pick" - iTunes App Store
"What's Hot!" - iTunes App Store
"I've fallen hard for Installation Magazine" - Design Milk
Installation Magazine features the work of established, mid-career and emerging artists. We juxtapose thoughtful analysis with visceral, lucid, and imaginative photography and art direction.
Subscribe for free and enjoy receiving emerging artists, innovative creative technology, and exclusive access to the world's most interesting people's private collections each week!
**Subscriptions may be managed or cancelled by going to your Account Settings within iTunes after purchase.
***We are here to support artists and help broaden our readers understanding of emerging contemporary art themes, people and passions. For any question, concerns, and ideas - please reach us directly at email@example.com of via Twitter @installationmag - we respond to every inquiry as soon as possible!
To view our privacy policy, visit http://www.installationmag.com/privacypolicy
To view our FAQ and guide to the App, visit http://www.installationmag.com/faq
What's New in Version 3.5
- Updated loading screen and visual presentation.
- Bug Fixes and New Logo!
Customer Reviews
Where is the magazine?
Theres no way to download issues!
Visual addiction. I love this.
- Free
- Category: Lifestyle
- Updated: Nov 26, 2013
- Version: 3.5
- Size: 9.0 MB
- Language: English
- Seller: Installation Media LLC
- © 2013 Installation Media, LLC
Compatibility: Requires iOS 5.1 or later. Compatible with iPhone, iPad, and iPod touch. This app is optimized for iPhone 5.


text:
The Top 125 Political Websites On The Net Version 3.0
Once again, I've decided it's time to rank the most popular political websites using: Alexa.com…
Do keep in mind that…
1) While I think this is a fairly comprehensive list, I'm sure some pages were probably left out. That wasn't intentional, it's just something that is going to happen when you try to take on an undertaking as massive as this one.
2) For a variety of reasons, I did not include websites run by either the Republican or Democratic party, newspapers, or pages run by the networks. Also, it goes without saying that sometimes I had to make judgement calls about whether certain pages that don't necessarily exclusively cover politics were going to make the list.
3) Alexa can't properly read all the: Blog*spot: pages so they all receive the same rank. Because of that, none of them could be included.
4) Last but not least, Alexa sometimes cannot distinguish between the main page and secondary page of a website. For example, Alexa gives: Eric Alterman's blog: the same ranking as: MSNBC: which meant I had to leave out a few blogs of interest that were hosted on much larger pages.
All 125 websites are listed with their Alexa rank following their link. So for example, a "1" would mean the page was the most popular website on the net. A "50,000" would mean the 50,000th most popular page on the net. With that being said, let's go ahead and take a look at the rankings.
(***After you're done, you may want to check previous editions of these rankings for comparison's sake***)
Top 100 Political Websites — January 2003
The Top 50 Bloggers — June 2003
Top 125 Political Websites — October 2003
The Top 50 Bloggers — February 2004
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text: Sale closed See all from Busy Being - Gardening, Notebook Busy Being - Gardening, Notebook ACHICA Loading Reserved. Please try again later. Sold Out In your basket This notebook features thoughtful, witty comments with a colourful typographic design that is sure to bring a smile to your face every time you use it. The spiral bound A5 notebook has 50 plain pages. Perfect for all your doodles, it would also make a lovely gift. Product Code: MTR-BB-GARDEN-NOTE Variant Code: : Delivery Returns Help You will be sent an email as soon as your order is placed. For more information please click here. We hope you will be thrilled with your purchases but in the unlikely event that you want to return something, you can find full return instructions on the back of your delivery note or click here. If you need assistance, please contact us here


text:
First Cash Nominates Ambassador Jorge Montano To Its Board Of Directors
ARLINGTON, Texas, May 3, 2010 (GLOBE NEWSWIRE) -- First Cash Financial Services, Inc. (Nasdaq:FCFS) announced that its Board of Directors has nominated Ambassador Jorge Montano to stand for election as a director at the Company's Annual Meeting of shareholders on June 16, 2010.
Ambassador Montano, age 64, is a native and resident of Mexico, where he has served in a variety of senior diplomatic positions and business consulting roles. Amb. Montano's long diplomatic career includes service as Ambassador of Mexico to the United States, permanent representative of Mexico to the United Nations and Assistant Secretary of Foreign Affairs. He currently serves as president of Asesoria y Analisis, a Mexico-based consulting and lobbying firm, a position he has held since 1995.
"We are very pleased that Amb. Jorge Montano has accepted the nomination to join our Board," said Mr. Rick Wessel, vice chairman and chief executive officer for First Cash. "We believe that his extensive knowledge and experience regarding business, political and governmental affairs in Mexico makes him uniquely qualified to serve as director for First Cash, given the Company's significant operations in Mexico and plans for continued growth in Latin America."
Amb. Montano has a doctoral degree in International Affairs from the London School of Economics and he has been a professor of International Organizations at Instituto Tecnologico Autonomo de Mexico (ITAM) since 1996. He is also the Vice President of the Mexican Council on Foreign Affairs and was recently elected by the United Nations General Assembly to serve as one of twelve members of the International Narcotics Control Board in Vienna, Austria. The nomination of Amb. Montano to the Board would fill the vacancy resulting from the decision of Ms. Tara MacMahon not to seek re-election to the Board. Ms. MacMahon will serve out her existing term as a director which expires on June 16, 2010, the date of the Annual Meeting. "Tara has been instrumental in positioning the business for long-term profitable growth and we would like to thank her for her many years of dedicated service as a member of our board," said Mr. Phillip Powell, Chairman of the Board of Directors at First Cash.Check Out Our Best Services for Investors
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text:
Lawmakers, Abortion Opponents React to Komen Reversal
Abortion opponents say they're disappointed by Susan G. Komen for the Cure's decision to reverse its policy of prohibiting grants to Planned Parenthood.
Full StoryJustin Dehn is a multimedia producer at the Tribune whose focus is videography and television production. He's been shooting TV news for more than a decade, most recently as a staff photojournalist at Austin's KVUE-TV from 2006 to 2010. At the Tribune, Dehn has been behind the camera for Tribune franchises Stump Interrupted, Face-Off and all the TribLive conversations, as well the stories we've produced each week for our dozen TV partners around the state.
Abortion opponents say they're disappointed by Susan G. Komen for the Cure's decision to reverse its policy of prohibiting grants to Planned Parenthood.
Full StoryLegislators and abortion opponents cheered the Susan G. Komen for the Cure's original decision to cut funding for Planned Parenthood.
Full StoryMorgan Smith explains how some parents are choosing not to let their children take the new and rigorous STAAR tests. And Kate Galbraith tells us how state officials may use money from BP for coastal restoration.
Full StoryA new poll shows Gov. Rick Perry's approval numbers in Texas are tanking, but his loyalists are quick to make the case for why he remains a powerful force.
Full StoryEmily Ramshaw explains how family planning clinics are complying with the state's new abortion sonogram law. Morgan Smith shows us a school district that is canceling its sports program.
Full StoryEvan Smith and Emily Ramshaw discuss Gov. Rick Perry's exit from the presidential race and look ahead at what Texans can expect now that their leader is returning from five months on the campaign trail.
Full StoryEvan Smith and Emily Ramshaw talk about why Gov. Rick Perry called it quits. Ross Ramsey talks about Ron Paul's appeal and his chances of winning the GOP nomination.
Full StoryIn this episode of Weekend Insider, reporter Brandi Grissom looks at the challenges that courts face when trying old cases. And Becca Aaronson tells us how proposed regulations on Internet businesses could affect Texas' booming technology sector.
Full StoryPerry, who came in fifth place in the Iowa caucuses, says he didn't come to South Carolina to "come in second." But the governor declined to say what whether he would give up and go home if he doesn't win the Palmetto State.
Full StoryTexas Gov. Rick Perry, trying to claw his way back in the 2012 presidential race, conjured up images of hungry vultures Tuesday to describe Mitt Romney's past as a corporate takeover artist. Perry rejected the notion that Romney was simply acting on free market principles when he led Bain Capital.
Full StoryGov. Rick Perry honed his combative southern voice Sunday, promising to make a fiery stand in the state where he first announced his presidential ambitions: South Carolina.
Full StoryDuring his speech, Gov. Rick Perry said he was going home for some "prayer and reflection" to decide whether to continue his presidential quest after a dismal fifth-place finish in the Iowa caucuses on Tuesday.
Full StoryDespite a disappointing fifth-place finish in Tuesday night's Iowa caucuses, which led Gov. Rick Perry to say he was returning to Texas to reassess his candidacy, he surprised everyone — including some of his own staff — by announcing that he would stay in the race.
Full StoryGov. Rick Perry is heading home for some "prayer and reflection" after his fifth-place finish in the Iowa caucuses. He'll decide whether to continue his presidential campaign over the next couple of days.
Full StoryGov. Rick Perry spent Tuesday doing everything he could to convince anyone he met to "have his back" in the Iowa caucuses. The Tribune talked with voters, decided and undecided, at caucus locations in Des Moines.
Full Story


text:
Nina Simone
Randy Newman
Beat-up little seagull
On a marble stair
Tryin' to find the ocean
Lookin' everywhere
Hard times in the city
In a hard town by the sea
Ain't nowhere to run to
There ain't nothin' here for free
Hooker on the corner
Waitin' for a train
Drunk lyin' on the sidewalk
Sleepin' in the rain
And they hide their faces
And they hide their eyes
'Cause the city's dyin'
And they don't know why
Oh, Baltimore
Man, it's hard just to live
Oh, Baltimore
Man, it's hard just to live, just to live
Get my sister Sandy
And my little brother Ray
Buy a big old wagon
Gonna haul us all away
Livin' in the country
Where the mountain's high
Never comin' back here
'Til the day I die
Oh, Baltimore
Man, it's hard just to live
Oh, Baltimore
Man, it's hard just to live, just to live
Writer(s): Randy Newman
Copyright: Six Pictures Music
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text:
This is my Final project at Vancouver film school for 3d animation and VFX program.
All visual elements: Arian ra lighting by: Len de Gracia
software used: Maya 2012 Mudbox 2012 Adobe Photoshop CS5 Nuke X 6.3v6
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text:
Your Health Has Met Its App(SM): Cigna Launches Health Plan App For Apple And Android Devices
BLOOMFIELD, Conn., Feb. 12, 2013 /PRNewswire/ -- Starting today, your smart phone can make you smarter about your health. Cigna is launching a new mobile app to help customers take better control of their health and health spending.
To view the multimedia content associated with this release, please click: http://www.multivu.com/players/English/59191-cigna-mobile-app/
(Photo: http://photos.prnewswire.com/prnh/20130212/MM44633 )
The myCigna Mobile App allows Cigna health plan customers instant access to their personalized health care information to:
- find a doctor using Cigna's award-winning physician directory,
- access ID cards in one convenient location,
- view and manage account balances and deductibles,
- look up medical, dental and pharmacy claims,
- research and compare drug prices at 60,000 pharmacies.
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Portfolio Manager Jim Cramer and Director of Research Jack Mohr reveal their investment tactics while giving advanced notice before every trade.
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- $2.5+ million portfolio
- Large-cap and dividend focus
- Intraday trade alerts from Cramer
Quant Ratings
Access the tool that DOMINATES the Russell 2000 and the S&P 500.
Product Features:
- Buy, hold, or sell recommendations for over 4,300 stocks
- Unlimited research reports on your favorite stocks
- A custom stock screener
Stocks Under $10
David Peltier uncovers low dollar stocks with serious upside potential that are flying under Wall Street's radar.
Product Features:
- Model portfolio
- Stocks trading below $10
- Intraday trade alerts
Check Out Our Best Services for Investors
Dividend Stock Advisor
David Peltier identifies the best of breed dividend stocks that will pay a reliable AND significant income stream.
Product Features:
- Diversified model portfolio of dividend stocks
- Updates with exact steps to take - BUY, HOLD, SELL
Trifecta Stocks
Every recommendation goes through 3 layers of intense scrutinyquantitative, fundamental and technical analysisto maximize profit potential and minimize risk.
Product Features:
- Model Portfolio
- Intra Day Trade alerts
- Access to Quant Ratings
Options Profits
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Product Features:
- Actionable options commentary and news
- Real-time trading community


text:
Winnipeg Free Press - PRINT EDITION
Pity not the typical pensioner
VANCOUVER -- Popular depictions of seniors, of whom I am one, often fail to reflect dramatically changing demographic and economic realities. Seniors are portrayed as surviving on fixed incomes with shrinking purchasing power, living in poorly heated apartments and eating endless macaroni and cheese dinners, sometimes without the cheese. While this portrayal of a vulnerable population captures the unfortunate circumstances of too many seniors, it also masks a very different larger reality.
Yes, seniors have relatively low incomes, but income should not be confused with wealth. Seniors who are mortgage free, who own their own homes, vehicles and in some cases vacation properties, are doing just fine.
Nor are their incomes necessarily fixed. Today's seniors, but likely not tomorrow's seniors, often enjoy private and public pensions indexed to the cost of living. Moreover, potentially higher interest rates will play out quite differently among those with no mortgages and substantial investments than they will among their younger compatriots.
The mismatch between cultural portrayals and economic reality has important political consequences. It allows seniors to wrap themselves in the flag of outdated stereotypes while obscuring their substantial political clout.
And, when it comes to resources, seniors are well-equipped to defend their interests in the political arena. They have the numbers, money, contacts, professional expertise and time needed for political engagement. They also vote at a much higher rate than do younger Canadians who are preoccupied employment and family concerns.
Although the political warriors of our time are commonly portrayed as 20-somethings immersed in the new world of social media, they are no match for the grandmother on the beach in Mexico, armed with the latest tablet (electronic, not medicinal), ample disposable wealth to support her causes, 850 friends on Facebook and the email addresses of federal, provincial and municipal leaders at the ready on her phone.
To use a phrase currently in vogue, today's pensioner is not your mother's pensioner.
Thus, while political leaders appear to worry incessantly about how to attract the elusive youth vote, they are always looking over their shoulder at the larger and more easily mobilized grey electorate.
Lingering cultural portrayals of seniors from previous times make it difficult for younger generations to press the argument that if there is truly a generation at risk, it is their generation that needs all the help it can get.
Youth who are audacious enough to promote their own interests, who are struggling with the challenges of young families, a difficult housing market and a very uncertain economy are dismissed as unappreciative and ungrateful.
As the senior population grows, as the infamous baby boomers begin to queue up for public benefits, we need to re-examine the financial benefits we confer too often on no longer poor pensioners.
My concern here is not with the myriad benefits that accrue to seniors from the private sector -- everything from reduced movies tickets and discounts at the neighbourhood liquor store to early-bird specials at restaurants.
Private companies should do what they want to pursue customers, although it is interesting to note seniors are the only demographic singled out for special treatment.
My concern lies with public benefits available to all seniors regardless of their financial standing.
Income supports for seniors -- transit passes, reduced property taxes in many jurisdictions, lower taxes for investment income than for employment income -- are walled off from vigorous policy debate.
The health-care costs for an aging population are largely borne by those generations who are still working.
To a modest degree, these benefits shift the tax burden from wealthy seniors to struggling young families. Our policy stance toward seniors is based on laudable compassion that too often ignores radically transformed demographic and economic realities.
Fortunately, there is a silver lining to this rather gloomy analysis, one that may forestall looming generational conflict.
If the grandmothers discussed above are well-equipped to defend their own interests in the political realm, just watch if the interests of their children and particularly grandchildren are threatened.
The best line of defence for Generations X, Y and Z may well be the grandmothers on the beach in Mexico, tablets powered up and chequebooks at the ready.
Troy Media columnist Dr. Roger Gibbins is the former president and CEO of the Canada West Foundation.
-- troymedia.com
Republished from the Winnipeg Free Press print edition July 25, 2013 A13
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Modern Family
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Modern Family Season 2 Episode 19
The Musical Man
Phil had a new promotional design prepared for his real estate services and graphically imaged onto the family van. The way it was depicted seemed to indicate that Claire and Haley were available for services altogether different from those of a typical realtor.
Cam took over the children's musical at Luke and Manny's school and overproduced it, much to the ire of the children. Cam chided Mitchell into keeping his mouth shut with regard to any criticism. The play ended with an inadvertent advocation of the F-word by the children.
Jay's brother Donny came to visit. Their terrorizing of one another drove Gloria to chastise Jay for the lack of love between them. Jay reached out to Donny to find out Donny has been having treatments for cancer. Donny didn't want to tell Jay because he didn't want to be treated with kid gloves.
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Pseudo-Banyan Optical WDM Packet Switching System With Near-Optimal Packet Scheduling
Journal of Optical Communications and Networking, Vol. 1, Issue 3, pp. B1-B14 (2009)
Enhanced HTML Acrobat PDF (1094 KB)
We present a novel pseudo-Banyan optical packet switching system (SBOPSS) for optical wavelength division multiplexing (WDM) networks. The system includes a group of pseudo-Banyan space switches together with single-stage downsized fiber-delay-line-based optical buffers. SBOPSS is scalable, with the result that each pseudo-Banyan space switch performs packet switching only for a cluster of wavelengths. The downsized optical buffers that are shared by output ports via the use of a small number of internal wavelengths result in efficient reduction in packet loss. Essentially, SBOPSS employs a packet scheduling algorithm, referred to as the parallel and incremental packet scheduler (PIPS). Given a set of newly arriving packets per time slot, PIPS determines a maximum number of valid paths (packets) to be scheduled with the current buffers' state taken into account. The algorithm aims at maximizing the system throughput subject to satisfying three constraints, which are switch-contention free, buffer-contention free, and sequential delivery. Significantly, we prove that PIPS is incremental in the sense that the computed-path sets are monotonically nondecreasing over time. We then propose a hardware parallel system architecture for the implementation of PIPS. As is shown, PIPS achieves a near-optimal solution with an exceptionally low computational complexity,
© 2009 Optical Society of America
OCIS Codes
(060.4230) Fiber optics and optical communications : Multiplexing
ToC Category:
Architectures and Technologies for Ultra-High Capacity Switched and Routed Optical Networks
Original Manuscript: November 28, 2008
Revised Manuscript: March 30, 2009
Manuscript Accepted: May 3, 2009
Published: July 28, 2009
Maria C. Yuang, Po-Lung Tien, and Shih-Hsuan Lin, "Pseudo-Banyan Optical WDM Packet Switching System With Near-Optimal Packet Scheduling," J. Opt. Commun. Netw. 1, B1-B14 (2009)
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- K. Obermann, S. Kindt, D. Breuer, K. Petermann, "Performance analysis of wavelength converters based on cross-gain modulation in semiconductor-optical amplifiers," J. Lightwave Technol., vol. 16, no. 1, pp. 78–85, Jan. 1998. [CrossRef]
- B. Sarker, T. Yoshino, S. Majumder, "All-optical wavelength conversion based on cross-phase modulation (XPM) in a single-mode fiber and a Mach–Zehnder interferometer," IEEE Photon. Technol. Lett., vol. 14, no. 3, pp. 340–342, March 2002. [CrossRef]
- F. Yan, W. Hu, W. Sun, W. Guo, Y. Jin, H. He, Y. Dong, "Placements of shared wavelength converter groups inside a cost-effective permuted Clos network," IEEE Photon. Technol. Lett., vol. 19, no. 13, pp. 981–983, July 2007. [CrossRef]
- V. Eramo, M. Listanti, "Packet loss in a bufferless optical WDM switch employing shared tunable wavelength converters," J. Lightwave Technol., vol. 18, no. 12, pp. 1818–1833, Dec. 2000. [CrossRef]
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