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Titlebook: Computer Vision - ECCV 2002; 7th European Confere Anders Heyden,Gunnar Sparr,Peter Johansen Conference proceedings 2002 Springer-Verlag Ber

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書目名稱Computer Vision - ECCV 2002
副標(biāo)題7th European Confere
編輯Anders Heyden,Gunnar Sparr,Peter Johansen
視頻videohttp://file.papertrans.cn/235/234298/234298.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision - ECCV 2002; 7th European Confere Anders Heyden,Gunnar Sparr,Peter Johansen Conference proceedings 2002 Springer-Verlag Ber
描述Premiering in 1990 in Antibes, France, the European Conference on Computer Vision, ECCV, has been held biennially at venues all around Europe. These conferences have been very successful, making ECCV a major event to the computer vision community. ECCV 2002 was the seventh in the series. The privilege of organizing it was shared by three universities: The IT University of Copenhagen, the University of Copenhagen, and Lund University, with the conference venue in Copenhagen. These universities lie ¨ geographically close in the vivid Oresund region, which lies partly in Denmark and partly in Sweden, with the newly built bridge (opened summer 2000) crossing the sound that formerly divided the countries. We are very happy to report that this year’s conference attracted more papers than ever before, with around 600 submissions. Still, together with the conference board, we decided to keep the tradition of holding ECCV as a single track conference. Each paper was anonymously refereed by three different reviewers. For the nal selection, for the rst time for ECCV, a system with area chairs was used. These met with the program chairsinLundfortwodaysinFebruary2002toselectwhatbecame45oralpres
出版日期Conference proceedings 2002
關(guān)鍵詞3D reconstruction; Stereo; algorithmic learning; algorithms; calibration; clustering; cognition; computatio
版次1
doihttps://doi.org/10.1007/3-540-47979-1
isbn_ebook978-3-540-47979-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2002
The information of publication is updating

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Dynamic Trees: Learning to Model Outdoor Scenes a forest of one or more tree-structured belief networks (TSBN). In the literature standard tree-structured belief network models have been found to produce “blocky” segmentations when naturally occurring boundaries within an image did not coincide with those of the subtrees in the fixed structure o
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Self-Organization of Randomly Placed Sensorseven if they do not have an overlapping field of view. (If the view overlapped, then standard stereo auto-calibration can be used.) This paper shows that the problem is solveable. Distant moving features allow accurate orientation registration. Given the sensor orientations, nearby linearly moving f
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