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Titlebook: Computer Vision -- ACCV 2012; 11th Asian Conferenc Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi Hu Conference proceedings 2013 Springer-Verlag

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書目名稱Computer Vision -- ACCV 2012
副標(biāo)題11th Asian Conferenc
編輯Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi Hu
視頻videohttp://file.papertrans.cn/235/234107/234107.mp4
概述Up-to-date results in computer vision.Fast-track conference proceedings.State-of-the-art research
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision -- ACCV 2012; 11th Asian Conferenc Kyoung Mu Lee,Yasuyuki Matsushita,Zhanyi Hu Conference proceedings 2013 Springer-Verlag
描述The four-volume set LNCS 7724--7727 constitutes the thoroughly refereed post-conference proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation, and recognition; segmentation, grouping, and classification; image representation; image and video retrieval and medical image analysis; face and gesture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.
出版日期Conference proceedings 2013
關(guān)鍵詞information retrieval; large datasets; machine learning; multi-view stereo; video segmentation
版次1
doihttps://doi.org/10.1007/978-3-642-37331-2
isbn_softcover978-3-642-37330-5
isbn_ebook978-3-642-37331-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

書目名稱Computer Vision -- ACCV 2012影響因子(影響力)




書目名稱Computer Vision -- ACCV 2012影響因子(影響力)學(xué)科排名




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,The Basis of the ‘Golden Age’,n visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improve
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The Forging of Gas Turbine Discs,es the advantages of accurately detecting objects or parts via chamfer matching and the robustness of a max-margin learning. Our results on standard benchmark datasets show that our method significantly outperforms current directional chamfer matching, thus redefining the state-of-the-art in this fi
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