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影响因子(影响力)




书目名称Computer Vision -- ECCV 2014影响因子(影响力)学科排名




书目名称Computer Vision -- ECCV 2014网络公开度




书目名称Computer Vision -- ECCV 2014网络公开度学科排名




书目名称Computer Vision -- ECCV 2014被引频次




书目名称Computer Vision -- ECCV 2014被引频次学科排名




书目名称Computer Vision -- ECCV 2014年度引用




书目名称Computer Vision -- ECCV 2014年度引用学科排名




书目名称Computer Vision -- ECCV 2014读者反馈




书目名称Computer Vision -- ECCV 2014读者反馈学科排名

使饥饿

Loss and Care: Asian Australian Documentarypre-completion and outliers removal, etc. Experiments demonstrate that our approach achieves state-of-the-art performance for the noisy image completion problem in terms of both PSNR and subjective visual quality.

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擦掉

The Dogaressa of Venice, 1200-1500ian face method with the learned face prior can handle the complex intra-personal variations such as large poses and large occlusions. Experiments on the challenging LFW benchmark shows that our algorithm outperforms most of the state-of-art methods.

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FLASK

The Base and the Superstructure of Societybases have demonstrated that the FDM outperforms the state-of-the-art methods for facial expression analysis. More importantly, the FDM achieves an impressive performance in a cross-database validation, which demonstrates the generalization capability of the selected features.

幸福愉悦感

,Two Tiers — and a Black Market,equency details from example scenes. Combining these two key strategies we can qualitatively and quantitatively outperform leading generic non-blind deconvolution methods when context-appropriate example images are available. We also compare to recent work which, like ours, tries to make use of cont

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Schwarps: Locally Projective Image Warps Based on 2D Schwarzian DerivativesD functions using differential properties of homographies. We name as Schwarp a warp which is estimated by penalizing the residual of Schwarzian equations. Experimental evaluation shows that Schwarps outperform existing warps in modeling and extrapolation power, and lead to far better results in Sha