By Julien Bect, Laure Blanc-Féraud, Gilles Aubert, Antonin Chambolle (auth.), Tomás Pajdla, Jiří Matas (eds.)
Welcome to the lawsuits of the eighth ecu convention on machine - sion! Following a truly profitable ECCV 2002, the reaction to our demand papers was once virtually both powerful – 555 papers have been submitted. We authorised forty-one papers for oral and 149 papers for poster presentation. numerous techniques have been brought into the overview technique. First, the n- ber of application committee individuals used to be elevated to minimize their assessment load. We controlled to assign to application committee individuals not more than 12 papers. moment, we followed a paper rating process. software committee participants have been requested to rank the entire papers assigned to them, even those who have been reviewed via extra reviewers. 3rd, we allowed authors to reply to the studies consolidated in a dialogue related to the realm chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas good as to this system committee participants. Our target was once to supply the authors with maximal suggestions and to enable this system committee individuals understand how authors reacted to their studies and the way their studies have been or weren't re?ected within the ?nal selection. ultimately, we lowered the size of reviewed papers from 15 to twelve pages. ThepreparationofECCV2004wentsmoothlythankstothee?ortsofthe- ganizing committee, the world 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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Extra resources for Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV
M. Lillholm, M. Nielsen, and L. D. Griffin. Feature-based image analysis. International Journal of Computer Vision, 52:73–95, 2003. 13. T. Lindeberg. Scale-Space Theory in Computer Vision. Kluwer Academic Press, Boston, 1994. 14. M. Loog, M. Lillholm, M. Nielsen, and M. A. Viergever. Gaussian scale space from insufficient image information. In Scale Space Methods in Computer Vision, 4th International Conference, Scale-Space 2003, pages 757–769, Isle of Skye, 2003. 15. M. Nielsen, L. M. J. Florack, and R.
While gradient constancy assumptions have also been proposed in [23,22] in order to deal with the aperture problem in the scope of a local approach, their use within variational methods is novel. The experimental evaluation shows that our method yields excellent results. Compared to those in the literature, their accuracy is always significantly higher, sometimes even twice as high as the best value known so far. Moreover, the method proved also to be robust under a considerable amount of noise and computation times of only a few seconds per frame on contemporary hardware are possible.
Graphical Models and Image Processing, 57(5):420–431, 1995. 15. N. Y. Arsenin. Solutions of ill-posed problems. Winston and Wiley, 1977. nl Abstract. A novel generalization of linear scale space is presented. The generalization allows for a sparse approximation of the function at a certain scale. To start with, we first consider the Tikhonov regularization viewpoint on scale space theory . The sparsification is then obtained using ideas from support vector machines  and based on the link between sparse approximation and support vector regression as described in  and .