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Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

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书目名称Computer Vision -- ECCV 2014
副标题13th European Confer
编辑David Fleet,Tomas Pajdla,Tinne Tuytelaars
视频videohttp://file.papertrans.cn/235/234169/234169.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati
描述The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014.The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
出版日期Conference proceedings 2014
关键词3D; activity recognition and understanding; artificial intelligence; computational photography; computer
版次1
doihttps://doi.org/10.1007/978-3-319-10599-4
isbn_softcover978-3-319-10598-7
isbn_ebook978-3-319-10599-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Photo Uncroplects, reprojects, and composites a subset of Internet imagery taken near the reference into a larger image around the reference using the underlying scene geometry. The proposed Markov Random Field based approach is capable of handling large Internet photo collections with arbitrary viewpoints, dra
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Collaborative Facial Landmark Localization for Transferring Annotations Across Datasetsnto a super dataset, with a union of all landmark types computed in each image as output. Our approach is flexible, and our system can optionally use known landmarks in the target dataset to constrain the localization. Our novel pipeline is built upon variants of state-of-the-art facial landmark loc
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Weighted Block-Sparse Low Rank Representation for Face Clustering in Videos knowledge (the face track that each face belongs to, and the pairs of faces that appear in the same frame), the task is to partition the faces into a given number of disjoint groups, such that each group is associated with one subject. To deal with this problem, we propose a new method called weigh
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