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Titlebook: Computer Vision -- ECCV 2010; 11th European Confer Kostas Daniilidis,Petros Maragos,Nikos Paragios Conference proceedings 2010 Springer-Ver

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書(shū)目名稱(chēng)Computer Vision -- ECCV 2010
副標(biāo)題11th European Confer
編輯Kostas Daniilidis,Petros Maragos,Nikos Paragios
視頻videohttp://file.papertrans.cn/235/234153/234153.mp4
概述Fast-track conference proceedings
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision -- ECCV 2010; 11th European Confer Kostas Daniilidis,Petros Maragos,Nikos Paragios Conference proceedings 2010 Springer-Ver
描述The 2010 edition of the European Conference on Computer Vision was held in Heraklion, Crete. The call for papers attracted an absolute record of 1,174 submissions. We describe here the selection of the accepted papers: Thirty-eight area chairs were selected coming from Europe (18), USA and Canada (16), and Asia (4). Their selection was based on the following criteria: (1) Researchers who had served at least two times as Area Chairs within the past two years at major vision conferences were excluded; (2) Researchers who served as Area Chairs at the 2010 Computer Vision and Pattern Recognition were also excluded (exception: ECCV 2012 Program Chairs); (3) Minimization of overlap introduced by Area Chairs being former student and advisors; (4) 20% of the Area Chairs had never served before in a major conference; (5) The Area Chair selection process made all possible efforts to achieve a reasonable geographic distribution between countries, thematic areas and trends in computer vision. EachArea Chair was assigned by the Program Chairs between 28–32 papers. Based on paper content, the Area Chair recommended up to seven potential reviewers per paper. Such assignment was made using all rev
出版日期Conference proceedings 2010
關(guān)鍵詞biometrics; computational imaging; face recognition; gesture recognition; illumination; image alignment; i
版次1
doihttps://doi.org/10.1007/978-3-642-15561-1
isbn_softcover978-3-642-15560-4
isbn_ebook978-3-642-15561-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2010
The information of publication is updating

書(shū)目名稱(chēng)Computer Vision -- ECCV 2010影響因子(影響力)




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010被引頻次




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010年度引用




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010讀者反饋




書(shū)目名稱(chēng)Computer Vision -- ECCV 2010讀者反饋學(xué)科排名




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Doug Easterling,Howard Kunreuther a challenging, ill-posed problem. Human performance on tasks such as shadow detection and shape-from-shading is improved by adding colour and texture to surfaces. In particular, when a surface is painted with a textured pattern, correlations between local mean luminance and local luminance amplitud
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Siting a High-Level Waste Repositoryn - the maximum fraction of the diffuse color component (so called maximum diffuse chromaticity in the literature) in local patches in color images changes smoothly. Using this property, we can estimate the maximum diffuse chromaticity values of the specular pixels by directly applying low-pass filt
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Doug Easterling,Howard Kunreutheremplate can be used for (1) classification of artistic and daily portrait photographs, and (2) numerical aesthetic quality assessment of these photographs in lighting usage. For learning the template, we adopt Haar-like local lighting contrast features, which are then extracted from pre-defined area
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Dream as an Individual Map of Dilemma generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using a new feature selection algorithm for linear SVM, te
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