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Titlebook: Computer Vision - ACCV 2014 Workshops; Singapore, Singapore C. V. Jawahar,Shiguang Shan Conference proceedings 2015 Springer International

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31#
發(fā)表于 2025-3-27 01:02:49 | 只看該作者
Person Re-identification Using Clustering Ensemble Prototypesf each prototype depends upon the appearance characteristics of gallery instances. The estimation of k-NN classifier is employed to specify a prototype to a given probe image. The similarity measure computation is performed between the probe and a subset of gallery images, that shares the same proto
32#
發(fā)表于 2025-3-27 01:23:07 | 只看該作者
Evaluation of Smile Detection Methods with Images in Real-World Scenarios LL-SVM is verified to find a balance between accuracy and efficiency. And among all the features including pixel value intensity, Gabor, LBP and HOG features, we find that HOG features are the most appropriate features to detect smiling faces, which, combined with RBF kernel SVM, achieve an accurac
33#
發(fā)表于 2025-3-27 05:40:12 | 只看該作者
34#
發(fā)表于 2025-3-27 10:59:10 | 只看該作者
35#
發(fā)表于 2025-3-27 17:33:34 | 只看該作者
Conference proceedings 2015l Workshop on Video Segmentation in Computer Vision, the Workshop: My Car Has Eyes: Intelligent Vehicle with Vision Technology, the Third Workshop on E-Heritage, and the Workshop on Computer Vision for Affective Computing. LNCS 9010 contains the papers selected for the Workshop on Feature and Simila
36#
發(fā)表于 2025-3-27 18:11:04 | 只看該作者
37#
發(fā)表于 2025-3-27 22:13:23 | 只看該作者
38#
發(fā)表于 2025-3-28 04:40:35 | 只看該作者
Spectral Shape Decomposition by Using a Constrained NMF Algorithmken into account. Starting from a contaminated observation matrix, the objective is to reveal its low rank almost block diagonal form. Although the proposed technique is applied to shapes on the MPEG7 database, it can be extended to 3D objects. The preliminary results we have obtained are very promising.
39#
發(fā)表于 2025-3-28 07:02:28 | 只看該作者
40#
發(fā)表于 2025-3-28 11:55:54 | 只看該作者
The Cultural Sociology of Art and Musicto the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance against the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.
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