By Rhodri H. Davies, Carole J. Twining, Tim F. Cootes, John C. Waterton, Chris J. Taylor (auth.), Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen (eds.)
Premiering in 1990 in Antibes, France, the ecu convention on computing device imaginative and prescient, ECCV, has been held biennially at venues throughout Europe. those meetings were very profitable, making ECCV an enormous occasion to the pc imaginative and prescient group. ECCV 2002 was once the 7th within the sequence. The privilege of organizing it was once shared through 3 universities: The IT college of Copenhagen, the college of Copenhagen, and Lund college, with the convention venue in Copenhagen. those universities lie ¨ geographically shut within the bright Oresund zone, which lies in part in Denmark and in part in Sweden, with the newly outfitted bridge (opened summer time 2000) crossing the sound that previously divided the nations. we're more than pleased to record that this year’s convention attracted extra papers than ever earlier than, with round six hundred submissions. nonetheless, including the convention board, we determined to maintain the culture of retaining ECCV as a unmarried music convention. each one paper used to be anonymously refereed via 3 assorted reviewers. For the nal choice, for the rst time for ECCV, a procedure with quarter chairs was once used. those met with this system chairsinLundfortwodaysinFebruary2002toselectwhatbecame45oralpresentations and 181 posters.Also at this assembly the choice used to be made with no wisdom of the authors’identity.
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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 III
Acknowledgments. The authors wish to thanks Charless Fowlkes, Jitendra Malik, Andrew Ng, Lorenzo Torresani, Yair Weiss, and Alice Zheng for helpful discussions. We would also like to thank Haili Chui and Anand Rangarajan for useful insights and for providing the ﬁsh datasets. g. on a grid) and form L−1 p . Now generate a random vector u, set its last three components to zero, and normalize it. Compute the T diagonalization L−1 p = U ΛU , with the eigenvalues sorted in descending order. √ Finally, compute w = νU Λ1/2 u.
ECCV 2002, LNCS 2352, pp. 21–31, 2002. c Springer-Verlag Berlin Heidelberg 2002 22 G. Donato and S. Belongie problem. Finally, we discuss a novel application of the Nystr¨ om approximation  to the TPS mapping problem. g. [2,7,4]) as well as in graphics applications such as morphing. 2 Review of Thin Plate Splines Let vi denote the target function values at locations (xi , yi ) in the plane, with i = 1, 2, . . , p. In particular, we will set vi equal to the target coordinates (xi ,yi ) in turn to obtain one continuous transformation for each coordinate.
5. H. Davis, A. Khotanzad, D. Flamig, and S. Harms. A physics-based coordinate transformation for 3-d image matching. IEEE Trans. Medical Imaging, 16(3):317– 328, June 1997. 6. F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7(2):219–269, 1995. 7. M. J. D. Powell. A thin plate spline method for mapping curves into curves in two dimensions. In Computational Techniques and Applications (CTAC95), Melbourne, Australia, 1995. 8. J. Smola and B.