René Vidal, Yi Ma (auth.), Tomás Pajdla, Jiří Matas (eds.)'s Computer Vision - ECCV 2004: 8th European Conference on PDF

By René Vidal, Yi Ma (auth.), Tomás Pajdla, Jiří Matas (eds.)

ISBN-10: 3540219846

ISBN-13: 9783540219842

Welcome to the court cases of the eighth eu convention on desktop - sion! Following a really profitable ECCV 2002, the reaction to our demand papers was once virtually both robust – 555 papers have been submitted. We approved forty-one papers for oral and 149 papers for poster presentation. a number of concepts have been brought into the assessment strategy. First, the n- ber of application committee individuals was once elevated to minimize their overview load. We controlled to assign to software committee contributors not more than 12 papers. moment, we followed a paper score method. application committee participants have been requested to rank the entire papers assigned to them, even those who have been reviewed by means of extra reviewers. 3rd, we allowed authors to reply to the stories consolidated in a dialogue regarding the realm chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas good as to this system committee contributors. Our target was once to supply the authors with maximal suggestions and to allow this system committee contributors know the way authors reacted to their experiences and the way their stories have been or weren't re?ected within the ?nal selection. eventually, we decreased the size of reviewed papers from 15 to twelve pages. ThepreparationofECCV2004wentsmoothlythankstothee?ortsofthe- ganizing committee, the realm chairs, this system committee, and the reviewers. we're indebted to Anders Heyden, Mads Nielsen, and Henrik J. Nielsen for passing on ECCV traditions and to Dominique Asselineau from ENST/TSI who kindly supplied his GestRFIA convention software program. We thank Jan-Olof Eklundh and Andrew Zisserman for encouraging us to arrange ECCV 2004 in Prague.

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Additional resources for Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part I

Example text

Notice also that EM with the normal vectors initialized at random (EM) yields a nonzero error in the noise free case, because it frequently converges to a local minimum. In fact, our algorithm PDA outperforms EM. However, if we use PDA to initialize EM (PDA+EM), the performance of both EM and PDA improves, showing that our algorithm can be effectively used to initialize iterative approaches to motion segmentation. Furthermore, the number of iterations of PDA+EM is approximately 50% with respect to EM randomly initialized, hence there is also a gain in computing time.

For each 1 ≤ j ≤ N , draw samples Zj from (f ∗g)(X dz. j) 3. Calculate the weights wj = (f ∗ g)(Xj )p(Zj )/p(Xj ) and output {(Zj , wj )}. The algorithm first draws samples from p, just like the canonical one. 3 Hence 3 A slight variant can be obtained by employing a two-variable kernel function G(x, z) in place of g(x−z). Then in the algorithm (f ∗g)(Xj ) is replaced by f (z)G(x, z)dz. 20 P. Torma and Cs. Szepesv´ ari the name of the procedure. In the next step, weights are calculated so that (Zj , wj ) becomes a properly weighted pair for f, p.

Hence, the proposed sampling scheme is more efficient than the one used by SIR provided that f, p 2 ≥ f ∗ g, (f p) ∗ g + I(f ) f s. (1) Let us now specialize (1) to the case when g equals to the equidistant Dirac-comb defined earlier. By using harmonic analysis arguments, one gets m/2 f ∗ gm , f p ∗ gm → 1/(2π)I(f )I(f p), as m → ∞. Hence, f ∗ gm , 1 f p∗gm ∼ mπ I(f )I(f p). Hence, condition 1 can be approximated by < f, p >2 ≥ 1 mπ I(f )I(f p) + m I(f ) f ∞ . Here, we have used m instead of in order to emphasize the dependency of on m.

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Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part I by René Vidal, Yi Ma (auth.), Tomás Pajdla, Jiří Matas (eds.)


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