Computer Vision — ECCV 2002: 7th European Conference on by Arthur E. C. Pece, Anthony D. Worrall (auth.), Anders

By Arthur E. C. Pece, Anthony D. Worrall (auth.), Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen (eds.)

The 4 quantity set LNCS 2350/2351/2352/2353 constitutes the refereed complaints of the seventh eu convention on desktop imaginative and prescient, ECCV 2002, held in Copenhagen, Denmark, in could 2002. The 226 revised complete papers offered have been rigorously reviewed and chosen from a complete of round six hundred submissions. The 4 books supply topical sections on energetic and real-time imaginative and prescient, picture positive aspects, visible movement, floor geometry, grouping and segmentation, stereoscopic imaginative and prescient, constitution from movement, form, item popularity, colour and shading, imaginative and prescient structures, statistical studying, robotic imaginative and prescient, and calibration.

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The likelihood. The dynamical model is fairly standard and is analyzed in section 4. At this stage, only 3 pose parameters of the object being tracked need to be considered: the X and Y coordinates on the ground plane and the orientation θZ . The vector of these pose parameters is defined as y = (X, Y, θZ ). 1 Observation Model The observation model can itself be broken down into two components: – a geometric component which defines a pdf over image locations of contours, given (a) the pose parameters of the object, (b) a 3-D geometric model of the object including an estimate of its shape variability, and (c) the camera geometry; – a “coloring/shading” component which defines a pdf over grey-level differences between pixels, given the image locations of the contours.

No exact point matching is required. This is especially useful in wide baseline camera systems where exact point matching is very difficult due to self-occlusion and a substantial change in viewpoint. The second contribution is the development of a scheme for setting priors for use in segmentation of a view using bayesian classification. The scheme, which assumes knowledge of approximate shape and location of objects, dynamically assigns priors for different objects at each pixel so that occlusion information is encoded in the priors.

This research has been funded by NSF grant no. EIA9901249. Appendix In this section, we prove that, in the case of a convex object O, the point of intersection of the diagonals of the quadrilateral formed by backprojecting the end-points of corresponding segments of that convex object is guaranteed to lie inside the object; and that no other point can be guaranteed thus. We prove this with the help of an illustration showing the plane corresponding to the epipolar lines. (see Figure 9). Let and be the rays back-projected from the left and right ends of the segment as seen from the first camera.

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