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Titlebook: Computer Vision - ACCV 2010; 10th Asian Conferenc Ron Kimmel,Reinhard Klette,Akihiro Sugimoto Conference proceedings 2011 Springer Berlin H

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书目名称Computer Vision - ACCV 2010
副标题10th Asian Conferenc
编辑Ron Kimmel,Reinhard Klette,Akihiro Sugimoto
视频video
概述Up to date results.Fast conference proceedings.State-of-the-art report
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision - ACCV 2010; 10th Asian Conferenc Ron Kimmel,Reinhard Klette,Akihiro Sugimoto Conference proceedings 2011 Springer Berlin H
描述The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010.All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images,and capturing and analyzing the world‘s geometry while preparing the higher level image and shape understanding. Novel gemometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.
出版日期Conference proceedings 2011
关键词categorization; correspondence; geomentry; matching; object detection; object modeling; object recognition
版次1
doihttps://doi.org/10.1007/978-3-642-19315-6
isbn_softcover978-3-642-19314-9
isbn_ebook978-3-642-19315-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Berlin Heidelberg 2011
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https://doi.org/10.1007/978-1-349-27248-8hs, we apply the tree-reweighted (TRW) message passing which outperforms the belief propagation. In experiments, we show the efficiency of the proposed method on the 1D signal reconstructions and demonstrate the performance of the proposed method in three applications: image denoising, sub-pixel ste
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https://doi.org/10.1007/978-1-349-27248-8number of scene points. Moreover, the approach is conceptually simple and easy to implement. Tests on a variety of real data sets show that the proposed method performs well on noisy and cluttered scenes in which only small parts of the objects are visible.
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