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 machine imaginative and prescient, ECCV 2002, held in Copenhagen, Denmark, in may perhaps 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 lively 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 attractiveness, colour and shading, imaginative and prescient platforms, statistical studying, robotic imaginative and prescient, and calibration.
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Extra resources for Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part I
For simplicity, the shape variability is deﬁned in the image plane. For convedef nience, we deﬁne = ν − µ, We also deﬁne the prior pdf of deformations fD ( ): 1 − 2 exp 2 ZD 2σ √ where ZD = 2πσ is a normalization factor. C. D. 4) is the likelihood ratio of the hypotheses that there is a contour at location µ and that there is no contour. The likelihood ratio is given by fM (∆I | µ) FL (∆I) 1 = f −1 [∆I(j∆ν)] fD ( j ) ∆ν m j L R(∆I | µ) = (9) Clearly, the same value of µ maximizes both the marginalized pdf and the likelihood ratio.
Such a camera system would also not be able to identify people or determine other important statistics (like height or color distributions) and hence may not be very useful for many applications. Krumm et. al.  present an algorithm that has goals very similar to ours. They use stereo cameras and combine information from multiple stereo cameras (currently only 2) in 3D space. They perform background subtraction and then detect humanshaped blobs in 3D space. Color histograms are created for each person and are used to identify and track people over time.
The fact that the generative model leads to a simple marginalization technique is an attractive feature. Methods that involve feature detection at any stage should marginalize over all possible correspondences of image features to model features, compatible with a hypothesis pose, in order to compute the correct likelihood of the pose. Similarly, 2-D contour methods should marginalize over all parameters of the 2-D contours, compatible with a state of a 3-D object, in order to compute the correct likelihood of the state.