By Julien Bect, Laure Blanc-Féraud, Gilles Aubert, Antonin Chambolle (auth.), Tomás Pajdla, Jiří Matas (eds.)
Welcome to the court cases of the eighth eu convention on machine - sion! Following a truly winning ECCV 2002, the reaction to our demand papers was once nearly both robust – 555 papers have been submitted. We accredited forty-one papers for oral and 149 papers for poster presentation. a number of techniques have been brought into the assessment method. First, the n- ber of software committee participants was once elevated to lessen their evaluate load. We controlled to assign to application committee individuals not more than 12 papers. moment, we followed a paper score approach. application committee individuals have been requested to rank the entire papers assigned to them, even those who have been reviewed through extra reviewers. 3rd, we allowed authors to reply to the reports consolidated in a dialogue related to the world chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas good as to this system committee participants. Our goal was once to supply the authors with maximal suggestions and to permit this system committee individuals know the way authors reacted to their stories and the way their studies have been or weren't re?ected within the ?nal choice. eventually, we lowered 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 prepare ECCV 2004 in Prague.
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Additional info for Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV
M. J. Florack, R. Maas, and W. J. Niessen. Pseudo-linear scale-space theory. International Journal of Computer Vision, 31(2/3):247–259, April 1999. 4. F. Girosi. An equivalence between sparse approximation and support vector machines. Neural Computation, 10(6):1455–1480, 1998. 24 M. Loog 5. F. Girosi, M. Jones, and T. Poggio. Prior stabilizers and basis functions: From regularization to radial, tensor and additive splines. Technical Report AI Memo 1430, CBCL Paper 75, MIT, Cambridge, MA, 1993. 6.
Before ending this section, let us remark that we always have the existence of a minimizer of the functional (3) in the discrete setting. However the difficult point for the convergence proof comes from the fact that the minimum is non necessarily unique. 3 for more details on the notations). The projection onto the convex closed set where is associated to can be numerically computed by a fixed point method, based on results in . We build recursively a sequence in of vectors in the following way: we choose and for each we let and for each We have a sufficient condition ensuring the convergence of the algorithm: Theorem 5.
2 Numerical Approximation The preceding Euler-Lagrange equations are nonlinear in their argument A first step towards a linear system of equations, which can be solved with common numerical methods, is the use of fixed point iterations on w. In order to implement a multiscale approach, necessary to better approximate the global optimum of the energy, these fixed point iterations are combined with a downsampling strategy. 5 on each level, it is proposed here to use an arbitrary factor what allows smoother transitions from one scale to the next1.