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Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

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樓主: Myelopathy
11#
發(fā)表于 2025-3-23 12:47:58 | 只看該作者
A Non-local Method for Robust Noisy Image Completionpre-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.
12#
發(fā)表于 2025-3-23 16:47:08 | 只看該作者
Improved Motion Invariant Deblurring through Motion Estimationobject motion .. With real camera images, we demonstrate significant reductions in the artifacts by using the estimated motion for deblurring. We also quantify a 96% reduction in reconstruction error, relative to a floor established by exact PSF deconvolution, via simulation with a large test set of
13#
發(fā)表于 2025-3-23 19:38:03 | 只看該作者
Consistent Matting for Light Field Imageses were obtained by using the blue screen technique. A variety of experiments show that our proposed algorithm produces both visually and quantitatively high-quality matting results for light field images.
14#
發(fā)表于 2025-3-24 00:50:42 | 只看該作者
Consensus of Regression for Occlusion-Robust Facial Feature Localizationoverlapping occlusion regions. After localization, the occlusion state for each landmark point is estimated using a Gaussian MRF semi-supervised learning method. Experiments on both non-occluded and occluded face databases demonstrate that our approach achieves consistently better results over state
15#
發(fā)表于 2025-3-24 03:01:24 | 只看該作者
Learning the Face Prior for Bayesian Face Recognitionian 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.
16#
發(fā)表于 2025-3-24 08:44:49 | 只看該作者
Spatio-temporal Event Classification Using Time-Series Kernel Based Structured Sparsityion datasets that include spontaneous behavior and differ in degree of difficulty and type of ground truth coding. KSS outperformed both sparse and non-sparse methods that utilize complex image features and their temporal extensions. In the case of early facial event classification KSS had 10% highe
17#
發(fā)表于 2025-3-24 11:04:58 | 只看該作者
Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Exbases 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.
18#
發(fā)表于 2025-3-24 15:24:17 | 只看該作者
19#
發(fā)表于 2025-3-24 21:07:54 | 只看該作者
Learning a Deep Convolutional Network for Image Super-Resolutionke traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.
20#
發(fā)表于 2025-3-24 23:44:24 | 只看該作者
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