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Titlebook: Computer Vision - ECCV 2008; 10th European Confer David Forsyth,Philip Torr,Andrew Zisserman Conference proceedings 2008 Springer-Verlag Be

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書目名稱Computer Vision - ECCV 2008
副標(biāo)題10th European Confer
編輯David Forsyth,Philip Torr,Andrew Zisserman
視頻videohttp://file.papertrans.cn/235/234146/234146.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision - ECCV 2008; 10th European Confer David Forsyth,Philip Torr,Andrew Zisserman Conference proceedings 2008 Springer-Verlag Be
描述Welcome to the 2008EuropeanConference onComputer Vision. These proce- ings are the result of a great deal of hard work by many people. To produce them, a total of 871 papers were reviewed. Forty were selected for oral pres- tation and 203 were selected for poster presentation, yielding acceptance rates of 4.6% for oral, 23.3% for poster, and 27.9% in total. Weappliedthreeprinciples.First,sincewehadastronggroupofAreaChairs, the ?nal decisions to accept or reject a paper rested with the Area Chair, who wouldbeinformedbyreviewsandcouldactonlyinconsensuswithanotherArea Chair. Second, we felt that authors were entitled to a summary that explained how the Area Chair reached a decision for a paper. Third, we were very careful to avoid con?icts of interest. Each paper was assigned to an Area Chair by the Program Chairs, and each Area Chair received a pool of about 25 papers. The Area Chairs then identi?ed and rankedappropriatereviewersfor eachpaper in their pool, and a constrained optimizationallocated three reviewers to each paper. We are very proud that every paper received at least three reviews. At this point, authors were able to respond to reviews. The Area Chairs then needed to reac
出版日期Conference proceedings 2008
關(guān)鍵詞action recognition; aerial imagery; algorithms; edge detection; motion capture; multi body tracking; multi
版次1
doihttps://doi.org/10.1007/978-3-540-88682-2
isbn_softcover978-3-540-88681-5
isbn_ebook978-3-540-88682-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Learning Spatial Context: Using Stuff to Find Things improve our detection ability over state-of-the-art detectors. We also present a method for learning the active set of relationships for a particular dataset. We present results on object detection in images from the PASCAL VOC 2005/2006 datasets and on the task of overhead car detection in satelli
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Segmentation and Recognition Using Structure from Motion Point Clouds are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance.
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0302-9743 We are very proud that every paper received at least three reviews. At this point, authors were able to respond to reviews. The Area Chairs then needed to reac978-3-540-88681-5978-3-540-88682-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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