Image skeletonization promises to be a powerful complexity-cutting tool for compact shape description, patternrecognition, robot vision, animation, petrography pore space fluid flow analysis, model/analysis of bone/l...
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Image skeletonization promises to be a powerful complexity-cutting tool for compact shape description, patternrecognition, robot vision, animation, petrography pore space fluid flow analysis, model/analysis of bone/lung/circulation, and image compression for telemedicine. The existing image Skeletonization techniques using boundary erosion, distance coding, and Voronoi diagram are first overviewed to assess/compare their feasibility of extending from 2D to 3D. An efficient distance-based procedure to generate the skeleton of large, complex 3D images such as CT, MRI data of human organ is then described. The proposed 3D Voxel Coding (3DVC) algorithm, is based on Discrete Euclidean Distance Transform. Instead of actual distance, each interior voxel (3D pixel) in the 3D image object is labeled with an integer code according to its relative distance from the object border for computation efficiency. All center voxels, which are the furthest away from the object border, are then collected and thinned to form clusters. To preserve the topology of the 3D image object, a cluster-labeling heuristic is then applied to order the clusters, and to recursively connect the next nearest clusters, gradually reducing the total number of disjoint clusters, to generate one final connected skeleton for each 3D object. The algorithm provides a straightforward computation which is robust and not sensitive to noise or object boundary complexity. Because 3D skeleton may not be unique, several application-dependent skeletonization options will be explored for meeting specific quality/speed requirements, and perhaps to incorporate automatic machine intelligence decisions. Parallel version of 3DVC is also introduced to further enhance skeletonization speed.
The 2004 Workshop on POCV emphasized novel, far-reaching ideas in PO rather than extensive experimental validation. This was intended to stimulate more discussion and debate than is typically feasible at a conference ...
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A method is presented for robust tracking in highly cluttered environments. The method makes effective use of 3D depth sensing technology, resulting in illumination-invariant tracking. A few applications using trackin...
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To facilitate activity recognition, analysis of the scene at multiple levels of detail is necessary. Required prerequisites for our activity recognition are tracking objects across frames and establishing a consistent...
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To facilitate activity recognition, analysis of the scene at multiple levels of detail is necessary. Required prerequisites for our activity recognition are tracking objects across frames and establishing a consistent labeling of objects across cameras. This paper makes several innovative uses of the epipolar constraint in the context of activity recognition. We first demonstrate how we track heads and hands using the epipolar geometry. Next we show how the detected objects are labeled consistently across cameras and zooms by employing epipolar, spatial, trajectory, and appearance properties. Finally we show how our method, utilizing the multiple levels of detail, is able to answer activity recognition problems which are difficult to answer with a single level of detail.
Can we detect low dimensional structure in high dimensional data sets of images and video? The problem of dimensionality reduction arises often in computervision and patternrecognition. In this paper, we propose a n...
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Can we detect low dimensional structure in high dimensional data sets of images and video? The problem of dimensionality reduction arises often in computervision and patternrecognition. In this paper, we propose a new solution to this problem based on semidenite programming. Our algorithm can be used to analyze high dimensional data that lies on or near a low dimensional manifold. It overcomes certain Imitations of previous work in manifold learning, such as Isomap and locally linear embedding. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and solid objects.
Since video-cameras became affordable and computers became powerful enough to process video in real-time, we have started to see a tremendous interest from both academia and industry to the vision-based human-oriented...
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On-line help from a human actor will be exploited to facilitate computer perception. This paper proposes an innovative real-time algorithm - running on an active vision head - to build 3D scene descriptions from human...
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This paper presents a method of optimizing a data-dependent kernel by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way...
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We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In te...
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We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In terms of the transformation, the group is made of either many still images or frames of a video sequence. The object identity is either discrete- or continuous-valued. This probabilistic framework integrates all the evidence of the set and handles the localization problem, illumination and pose variations through subspace identity encoding. Issues and challenges arising in this framework are addressed and efficient computational schemes are presented. Good face recognition results using the PIE database are reported.
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