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Titlebook: Computer Vision -- ACCV 2014; 12th Asian Conferenc Daniel Cremers,Ian Reid,Ming-Hsuan Yang Conference proceedings 2015 Springer Internation

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書目名稱Computer Vision -- ACCV 2014
副標(biāo)題12th Asian Conferenc
編輯Daniel Cremers,Ian Reid,Ming-Hsuan Yang
視頻videohttp://file.papertrans.cn/235/234015/234015.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision -- ACCV 2014; 12th Asian Conferenc Daniel Cremers,Ian Reid,Ming-Hsuan Yang Conference proceedings 2015 Springer Internation
描述.The five-volume set LNCS 9003--9007 constitutes the thoroughly refereed post-conference proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, held in Singapore, Singapore, in November 2014..The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and gesture, tracking; stereo, physics, video and events; and poster sessions 1-3..
出版日期Conference proceedings 2015
關(guān)鍵詞Action recognition; Aerial images; Camera calibration; City-scale; Data mining; Differential constraints;
版次1
doihttps://doi.org/10.1007/978-3-319-16808-1
isbn_softcover978-3-319-16807-4
isbn_ebook978-3-319-16808-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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978-3-319-16807-4Springer International Publishing Switzerland 2015
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Daniel Cremers,Ian Reid,Ming-Hsuan YangIncludes supplementary material:
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The Cytoskeleton and the Cytoplasmic Matrixes on the alignment of the two coordinate systems. When positional measurements provided by a low-cost GPS are corrupted by high-level noises, this approach becomes problematic. In this paper, we introduce a novel framework that uses similarity invariants to form a tetrahedral network of views for t
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https://doi.org/10.1007/978-3-642-79482-7t from conventional autoencoders which reconstruct each sample from its encoding, we use the encoding of each sample to reconstruct its local neighbors. In this way, the learned representations are consistent among local neighbors and robust to small variations of the inputs. When trained with super
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