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Titlebook: Computer Vision – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V

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發(fā)表于 2025-3-21 18:41:05 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision – ECCV 2012
副標題12th European Confer
編輯Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi
視頻videohttp://file.papertrans.cn/235/234160/234160.mp4
概述Up to date results.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 – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V
描述The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
出版日期Conference proceedings 2012
關鍵詞Markov random fields; activity recognition; machine learning; object detectors; saliency models; algorith
版次1
doihttps://doi.org/10.1007/978-3-642-33709-3
isbn_softcover978-3-642-33708-6
isbn_ebook978-3-642-33709-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

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Statistics of Patch Offsets for Image Completionhat if we match similar patches in the image and obtain their offsets (relative positions), the statistics of these offsets are sparsely distributed. We further observe that a few dominant offsets provide reliable information for completing the image. With these offsets we fill the missing region by
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MatchMiner: Efficient Spanning Structure Mining in Large Image Collectionstions often require knowledge of the visual connectivity structure of these image collections—describing which images overlap or are otherwise related—and an important step in understanding this structure is to identify . of this underlying image graph. As the structure of this graph is often initia
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V1-Inspired Features Induce a Weighted Margin in SVMsisual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards . these architectures perform so well. In this paper, we offer a unique perspective to this qu
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