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Titlebook: Computer Vision -- ECCV 2012. Workshops and Demonstrations; Florence, Italy, Oct Andrea Fusiello,Vittorio Murino,Rita Cucchiara Conference

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書目名稱Computer Vision -- ECCV 2012. Workshops and Demonstrations
副標(biāo)題Florence, Italy, Oct
編輯Andrea Fusiello,Vittorio Murino,Rita Cucchiara
視頻videohttp://file.papertrans.cn/235/234163/234163.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. Workshops and Demonstrations; Florence, Italy, Oct Andrea Fusiello,Vittorio Murino,Rita Cucchiara Conference
描述The three volume set LNCS 7583, 7584 and 7585 comprises the Workshops and Demonstrations which took place in connection with the European Conference on Computer Vision, ECCV 2012, held in Firenze, Italy, in October 2012. The total of 179 workshop papers and 23 demonstration papers was carefully reviewed and selected for inclusion in the proceedings. They where held at workshops with the following themes: non-rigid shape analysis and deformable image alignment; visual analysis and geo-localization of large-scale imagery; Web-scale vision and social media; video event categorization, tagging and retrieval; re-identification; biological and computer vision interfaces; where computer vision meets art; consumer depth cameras for computer vision; unsolved problems in optical flow and stereo estimation; what‘s in a face?; color and photometry in computer vision; computer vision in vehicle technology: from earth to mars; parts and attributes; analysis and retrieval of tracked events and motionin imagery streams; action recognition and pose estimation in still images; higher-order models and global constraints in computer vision; information fusion in computer vision for concept recognition
出版日期Conference proceedings 2012
關(guān)鍵詞augmented reality; cognitive modeling; environment modeling; human-machine interaction; visual analysis;
版次1
doihttps://doi.org/10.1007/978-3-642-33863-2
isbn_softcover978-3-642-33862-5
isbn_ebook978-3-642-33863-2Series 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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3D Facial Landmark Localization Using Combinatorial Search and Shape Regression.g. from a feature detector) and performs combinatorial search constrained by a deformable shape model. A key assumption of our approach is that for some landmarks there might not be an accurate candidate in the input set. This is tackled by detecting partial subsets of landmarks and inferring those
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Statistical Shape Analysis for Population Studies via Level-Set Based Shape Morphing structures between populations. This is accomplished by a novel level-set framework for shape morphing and a . dissimilarity-measure derived by a modified version of the Hausdorff distance. The proposed method does not require explicit one-to-one point correspondences and is fast, robust and easy t
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Drawing an Automatic Sketch of Deformable Objects Using Only a Few Imagesg a part-based deformable contour matching technique gives the location of repeatable contours. An initial deformable search strategy selects a set of salient, repeatable contours robust to a large range of non-rigid deformations. A contour completion technique based on a locally greedy bi-direction
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Superfaces: A Super-Resolution Model for 3D Facesof 3D scans on the recognition process has not been addressed explicitly yet being of primal importance after the introduction of a new generation of low cost 4D scanning devices. These devices are capable of combined depth/rgb acquisition over time with a low resolution compared to the 3D scanners
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Stable Spectral Mesh Filteringn 3D data. It has been shown that the eigenfunctions of the Laplace-Beltrami operator (manifold harmonics) of a surface play the role of the Fourier basis in the Euclidean space; it is thus possible to formulate signal analysis and synthesis in the manifold harmonics basis. In particular, geometry f
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Analytical Dynamic Programming Matchingnomial time if the problem is formulated as a maximum a posteriori problem using Gaussian distributions for the likelihood and prior. After giving the derivation of the analytical DP matching algorithm, we evaluate its performance on handwritten character images containing various nonlinear deformat
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