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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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书目名称Computer Vision – ECCV 2012
副标题12th European Confer
编辑Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi
视频video
概述Up to date results.Fast track conference proceedings.State of the art research
丛书名称Lecture Notes in Computer Science
图书封面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
关键词Markov random fields; activity recognition; machine learning; object directors; saliency models; algorith
版次1
doihttps://doi.org/10.1007/978-3-642-33786-4
isbn_softcover978-3-642-33785-7
isbn_ebook978-3-642-33786-4Series 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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Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Artrt denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tract
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A Probabilistic Approach to Robust Matrix Factorizationand when there exist outliers and missing data. In this paper, we propose a novel probabilistic model called Probabilistic Robust Matrix Factorization (PRMF) to solve this problem. In particular, PRMF is formulated with a Laplace error and a Gaussian prior which correspond to an ℓ. loss and an ℓ. re
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