Multiple-look fusion is quickly becoming more important in statistical patternrecognition. With increased computing power and memory one can make many measurements on an object of interest using, for example, video i...
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Multiple-look fusion is quickly becoming more important in statistical patternrecognition. With increased computing power and memory one can make many measurements on an object of interest using, for example, video imagery or radar. By obtaining more views of an object, a system can make decisions with lower missed detection and false alarm errors. There are many approaches for combining information from multiple looks and we mathematically compare and contrast the sequential probability ratio test, Bayesian fusion, and Dempster-Shafer theory of evidence. Using a consistent probabilistic framework we demonstrate the differences and similarities between the approaches and show results for an application in infrared video classification.
A novel technique for computing intrinsic surface properties is developed. Intrinsic surface properties refer to those properties of a surface which are not affected by the choice of the coordinate system, the positio...
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A novel technique for computing intrinsic surface properties is developed. Intrinsic surface properties refer to those properties of a surface which are not affected by the choice of the coordinate system, the position of the viewer relative to the surface, and the particular parametric representation used to describe the imaged surface. Since intrinsic properties are characteristics of a surface, they are ideal for the purpose of representation and recognition. The intrinsic properties of interest are the principal curvatures, the intrinsic distance, and the lines of curvature. The authors adopt a structured lighting sensing configuration where a grid pattern is projected to encode the object surfaces for analysis. At each stripe junction, the curvature of the projected stripe on the object surface is computed and related to that of the normal section which shares the same tangential direction as the projected curve. The principal curvatures and their directions at the stripe junction under consideration are then recovered using Euler's theorem.< >
Although motion analysis has been extensively investigated in the literature and a wide variety of tracking algorithms have been proposed, the problem of tracking objects using the Dynamic vision Sensor requires a sli...
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Although motion analysis has been extensively investigated in the literature and a wide variety of tracking algorithms have been proposed, the problem of tracking objects using the Dynamic vision Sensor requires a slightly different approach. Dynamic vision Sensors are biologically inspired vision systems that asynchronously generate events upon relative light intensity changes. Unlike conventional vision systems, the output of such sensor is not an image (frame) but an address events stream. Therefore, most of the conventional tracking algorithms are not appropriate for the DVS data processing. In this paper, we introduce algorithm for spatiotemporal tracking that is suitable for Dynamic vision Sensor. In particular, we address the problem of multiple persons tracking in the occurrence of high occlusions. We investigate the possibility to apply Gaussian Mixture Models for detection, description and tracking objects. Preliminary results prove that our approach can successfully track people even when their trajectories are intersecting.
We propose a nonparametric framework based on the beta process for discovering temporal patterns within a heterogenous video collection. Starting from quantized local motion descriptors, we describe the long-range tem...
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We propose a nonparametric framework based on the beta process for discovering temporal patterns within a heterogenous video collection. Starting from quantized local motion descriptors, we describe the long-range temporal dynamics of each video via transitions between a set of dynamical behaviors. Bayesian nonparametric statistical methods allow the number of such behaviors and the subset exhibited by each video to be learned without supervision. We extend the earlier beta process HMM in two ways: adding data-driven MCMC moves to improve inference on realistic datasets and allowing global sharing of behavior transition parameters. We illustrate discovery of intuitive and useful dynamical structure, at various temporal scales, from videos of simple exercises, recipe preparation, and Olympic sports. Segmentation and retrieval experiments show the benefits of our nonparametric approach.
We propose four measures of image organizational change which can be used to monitor construction activity. The measures are based on the thesis that the progress of construction will see a change in the individual im...
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We propose four measures of image organizational change which can be used to monitor construction activity. The measures are based on the thesis that the progress of construction will see a change in the individual image feature attributes as well as an evolution in the relationships among these features. This change in the relationship is captured by the eigenvalues and eigenvectors of the relation graph embodying the organization among the image features. We demonstrate the ability of the measures to differentiate between no development, the onset of construction, and full development, on the available real test image set.
A technique for the estimation of 2-D normal velocity is presented. The image sequence is first represented by a family of velocity-tuned linear filters. Normal velocity, in the individual filter outputs, is expressed...
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A technique for the estimation of 2-D normal velocity is presented. The image sequence is first represented by a family of velocity-tuned linear filters. Normal velocity, in the individual filter outputs, is expressed as the local first-order behavior of surfaces of constant phase. Justification for this is discussed, and it is shown to provide an effective basis for the local computation of normal velocity. The resultant approach is local in space-time. It permits multiple velocity estimates within a single neighborhood, and it yields accurate velocity estimates that are robust with respect to noise and perspective deformation.< >
A stereo matching method is presented which uses multiple stereo pairs with various baselines to obtain precise depth estimates without suffering from ambiguity. The stereo matching method uses multiple stereo pairs w...
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A stereo matching method is presented which uses multiple stereo pairs with various baselines to obtain precise depth estimates without suffering from ambiguity. The stereo matching method uses multiple stereo pairs with different baselines generated by a lateral displacement of a camera. Matching is performed by computing the sum of squared-difference (SSD) values. The SSD functions for individual stereo pairs are represented with respect to the inverse depth (rather than the disparity, as is usually done), and then are simply added to produce the sum of SSDs. This resulting function is called the SSSD-in-inverse-depth. The authors define a stereo algorithm, based on the SSSD-in-inverse-depth and then present a mathematical analysis to show how the algorithm can remove ambiguity and increase precision. Experimental results for stereo images are presented to demonstrate the effectiveness of the algorithm.< >
Visual attention is the cognitive process of directing our gaze on one aspect of the visual field while ignoring others. The mainstream approach to modeling focal visual attention involves identifying saliencies in th...
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Visual attention is the cognitive process of directing our gaze on one aspect of the visual field while ignoring others. The mainstream approach to modeling focal visual attention involves identifying saliencies in the image and applying a search process to the salient regions. However, such inference schemes commonly fail to accurately capture perceptual attractors, require massive computational effort and, generally speaking, are not biologically plausible. This paper introduces a novel approach to the problem of visual search by framing it as an adaptive learning process. In particular, we devise an approximate optimal control framework, based on reinforcement learning, for actively searching a visual field. We apply the method to the problem of face detection and demonstrate that the technique is both accurate and scalable. Moreover, the foundations proposed here pave the way for extending the approach to other large-scale visual perception problems.
We present an interactive approach for segmenting thin volumetric structures. The proposed segmentation model is based on an anisotropic weighted Total Variation energy with a global volumetric constraint and is minim...
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We present an interactive approach for segmenting thin volumetric structures. The proposed segmentation model is based on an anisotropic weighted Total Variation energy with a global volumetric constraint and is minimized using an efficient numerical approach and a convex relaxation. The algorithm is globally optimal w.r.t. the relaxed problem for any volumetric constraint. The binary solution of the relaxed problem equals the globally optimal solution of the original problem. Implemented on today's user-programmable graphics cards, it allows real-time user interaction. The method is applied to and evaluated on the task of articular cartilage segmentation of human knee joints and segmentation of tubular structures like liver vessels and airway trees.
The authors consider the problem of occlusion in computing stereo disparity from a pair of images. Usual approaches to stereo matching, e.g. area-based, feature-based, etc., can perform poorly in the neighborhood of o...
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The authors consider the problem of occlusion in computing stereo disparity from a pair of images. Usual approaches to stereo matching, e.g. area-based, feature-based, etc., can perform poorly in the neighborhood of occluding boundaries if no surface models are assumed. Qualitative improvements are possible based on conditions derived from the geometry of occlusion. A pair of correspondence processes, using information near an occlusion boundary to localize the boundary, can determine the sign of associated depth discontinuity unambiguously. The proposed method is able to identify the presence and extent of occlusion regions and assign disparities in a consistent way near the occlusion regions.< >
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