Download Computer Vision — ECCV 2002: 7th European Conference on by Rhodri H. Davies, Carole J. Twining, Tim F. Cootes, John C. PDF

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 immense occasion to the pc imaginative and prescient neighborhood. ECCV 2002 used to be the 7th within the sequence. The privilege of organizing it used to be shared through 3 universities: The IT collage of Copenhagen, the collage of Copenhagen, and Lund collage, with the convention venue in Copenhagen. those universities lie ¨ geographically shut within the vibrant Oresund area, which lies in part in Denmark and partially in Sweden, with the newly equipped bridge (opened summer time 2000) crossing the sound that previously divided the nations. we're more than happy to file that this year’s convention attracted extra papers than ever ahead of, with round six hundred submissions. nonetheless, including the convention board, we determined to maintain the culture of maintaining ECCV as a unmarried tune convention. every one paper used to be anonymously refereed by means of 3 varied reviewers. For the nal choice, for the rst time for ECCV, a approach with region chairs was once used. those met with this system chairsinLundfortwodaysinFebruary2002toselectwhatbecame45oralpresentations and 181 posters.Also at this assembly the choice was once made with out wisdom of the authors’identity.

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Y. Weiss. Smoothness in layers: Motion segmentation using nonparametric mixture estimation. In Proc. IEEE Conf. Comput. Vision and Pattern Recognition, pages 520–526, 1997. 11. C. Williams and M. Seeger. Using the Nystr¨ om method to speed up kernel machines. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, pages 682–688, 2001. DEFORMOTION Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images Stefano Soatto1 and Anthony J.

N (4) DEFORMOTION 37 The pre-shape µ ˆ is called the shape average relative to the group G, or Gaverage, and the quantity gˆi−1 (γi ) is called the shape of γi . Remark 1 (Invariance) In the definition above, one will notice that the shape average is actually a pre-shape, and that there is an arbitrary choice of group action g0 that, if applied to γi and µ, leaves the definition unchanged (the functional E is invariant with respect to g0 because T (g ◦ g0 , h ◦ g0 ) = T (g, h) ∀ g0 ). For the case of the Euclidean group SE(N ), a way to see this is to notice that the reference frame where µ is described is arbitrary.

Then, since the directions are ordered in terms of non-increasing eigenvalue/variance, the total Description Length for our training set, and our Objective function, can be written thus: ng ng +nmin D(1) (σ p , ns , R, ∆) + F= p=1 D(2) (σ q , ns , R, ∆). (12) q=ng +1 We now consider the form of this objective function. For the linear model defined earlier (1): n 1 s m 2 np λ m = (y ) (13) ns i=1 i In the limit ∆ → 0, the quantised values in Yˆ approach their continuum values, so that: σ m → np λ m .

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