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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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發(fā)表于 2025-3-21 16:22:17 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computer Vision – ECCV 2012
副標(biāo)題12th European Confer
編輯Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi
視頻videohttp://file.papertrans.cn/235/234158/234158.mp4
概述Up to date results.Fast track conference proceedings.State of the art research
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)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
關(guān)鍵詞Markov random fields; activity recognition; machine learning; object detectors; saliency models; algorith
版次1
doihttps://doi.org/10.1007/978-3-642-33712-3
isbn_softcover978-3-642-33711-6
isbn_ebook978-3-642-33712-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

書(shū)目名稱(chēng)Computer Vision – ECCV 2012影響因子(影響力)




書(shū)目名稱(chēng)Computer Vision – ECCV 2012影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Computer Vision – ECCV 2012網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2012被引頻次




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書(shū)目名稱(chēng)Computer Vision – ECCV 2012年度引用




書(shū)目名稱(chēng)Computer Vision – ECCV 2012年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2012讀者反饋




書(shū)目名稱(chēng)Computer Vision – ECCV 2012讀者反饋學(xué)科排名




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Malcolm N. MacDonald,Duncan Hunterignment classifiers to further improve the accuracy. Extensive evaluations were performed on several datasets including the challenging Labeled Faces in the Wild (LFW). Face parts descriptors were also evaluated, including the recently proposed Minimum Output Sum of Squared Error (MOSSE) filter. The
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https://doi.org/10.1007/978-1-349-10452-9cing consistent annotations over similar visual patterns. Our model is optimized by efficient belief propagation algorithms embedded in an expectation-maximization (EM) scheme. Extensive experiments are conducted to evaluate the performance on several standard large-scale image datasets, showing tha
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https://doi.org/10.1057/9781137310903ide a weighted regret bound as a theoretical guarantee of performance. The proposed novel online learning framework can handle examples with different importance weights for binary, multiclass, and even structured output labels in both linear and non-linear kernels. Applying the method to tracking r
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The Discovery of Chinese Literature (Wenxue)solve this constrained minimization problem. In particular, manually annotated segmentation on a very small set of 2D slices are taken as constraints and incorporated into the whole clustering process. Experimental results demonstrate that the proposed CMEWCVT algorithm significantly improve the pre
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God, Group, and Blame Psychology,elated methods. The experimental evaluation demonstrates that state-of-the-art detection and segmentation results are achieved and that our method is inherently able to handle overlapping instances and an increased range of articulations, aspect ratios and scales.
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Annotation Propagation in Large Image Databases via Dense Image Correspondencecing consistent annotations over similar visual patterns. Our model is optimized by efficient belief propagation algorithms embedded in an expectation-maximization (EM) scheme. Extensive experiments are conducted to evaluate the performance on several standard large-scale image datasets, showing tha
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