By René Vidal, Yi Ma (auth.), Tomás Pajdla, Jiří Matas (eds.)
Welcome to the complaints of the eighth ecu convention on machine - sion! Following a really winning ECCV 2002, the reaction to our demand papers used to be nearly both powerful – 555 papers have been submitted. We permitted forty-one papers for oral and 149 papers for poster presentation. numerous options have been brought into the overview technique. First, the n- ber of application committee contributors was once elevated to minimize their overview load. We controlled to assign to application committee participants not more than 12 papers. moment, we followed a paper rating process. application committee individuals 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 studies consolidated in a dialogue concerning the world chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas good as to this system committee individuals. Our goal used to be to supply the authors with maximal suggestions and to permit 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 choice. ultimately, we decreased 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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Additional info for Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part I
The problem of tracking a varying number of non-rigid objects has two major diﬃculties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these diﬃculties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences.
N (z N )]T ∈ CN ×Mn (K) . One can show that there is a unique solution for c (up to a scale factor) if N ≥ Mn (K) − 1 and at least K − 1 points belong to each hyperplane. Furthermore, since the last entry of each bi is equal to one, then so is the last entry of c. Therefore, one can solve for c uniquely. In the presence of noise, one can solve for c in a least-squares sense as the singular vector of Ln associated with its smallest singular value, and then normalize so that cMn (K) = 1. A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation 5 Segmenting the multibody motion.
Once the image pairs have been clustered, the estimation of each homography, either real or complex, becomes a simple linear problem. Remark 1 (Direct extraction of homographies from H). There is yet another way to obtain individual Hi from H without segmenting the image pairs first. Once the complex epipoles ei are known, one can compute the following linear combination of the rows of Hi (up to scale) from the derivatives of the multibody homography constraint at ei w T Hi ∼ ∂νn (w)T Hνn (x) ∂x x=ei ∈ CP2 , ∀w ∈ C2 .