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By Sami Romdhani, Volker Blanz, Thomas Vetter (auth.), Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen (eds.)

ISBN-10: 3540437487

ISBN-13: 9783540437482

ISBN-10: 3540479791

ISBN-13: 9783540479796

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Read or Download Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV PDF

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Extra info for Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV

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Active blobs. In 6th ICCV, 1998. 21. T. Sim, S. Baker, and M. Bsat. The cmu pose, illumination and expression (pie) database of human faces. Technical Report CMU-RI-TR-01-02, CMU, 2000. it Abstract. Learning one class at a time can be seen as an effective solution to classification problems in which only the positive examples are easily identifiable. A kernel method to accomplish this goal consists of a representation stage - which computes the smallest sphere in feature space enclosing the positive examples - and a classification stage - which uses the obtained sphere as a decision surface to determine the positivity of new examples.

Spatial support of the Hausdorff distance. Both rows: the white pixels in the rightmost image show the locations of the leftmost image which are close, in the Hausdorff sense, to the middle image. for a given false-alarm rate, the better system will be the one with the higher hit probability. The overall performance of a system can be measured by the area under the curve. Figure 4 shows the ROC curves of a linear and Hausdorff kernels for all the four face recognition problems. The curve obtained with the Hausdorff kernel is always above the one of the linear kernel, showing superior performance.

A. Heyden et al. ): ECCV 2002, LNCS 2353, pp. 20–33, 2002. c Springer-Verlag Berlin Heidelberg 2002 Hausdorff Kernel for 3D Object Acquisition and Detection 21 In this paper, we introduce a kernel derived from an image matching technique based on the notion of Hausdorff distance [7], well suited to capture image similarities, while preserving meaningful image differences. The proposed kernel is tolerant to small amount of local deformations and scale changes, does not require accurate image registration or segmentation, and is well suited to deal with occlusion issues.

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Computer Vision — ECCV 2002: 7th European Conference on Computer Vision Copenhagen, Denmark, May 28–31, 2002 Proceedings, Part IV by Sami Romdhani, Volker Blanz, Thomas Vetter (auth.), Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen (eds.)


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