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