CASIA OpenIR

浏览/检索结果: 共5条,第1-5条 帮助

限定条件                    
已选(0)清除 条数/页:   排序方式:
Learning Stacked Image Descriptor for Face Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2016, 卷号: 26, 期号: 9, 页码: 1685-1696
作者:  Lei, Zhen;  Yi, Dong;  Li, Stan Z.
Adobe PDF(5197Kb)  |  收藏  |  浏览/下载:378/146  |  提交时间:2016/12/26
Deep Discriminant Face Representation  Face Recognition  Learning-based Descriptor  Stacked Image Descriptor (Sid)  
Adaptive Slice Representation for Human Action Classification 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2015, 卷号: 25, 期号: 10, 页码: 1624-1636
作者:  Shan, Yanhu;  Zhang, Zhang;  Yang, Peipei;  Huang, Kaiqi;  Kaiqi Huang
Adobe PDF(2720Kb)  |  收藏  |  浏览/下载:357/105  |  提交时间:2015/11/12
Action Recognition  Adaptive Slice  Mel Frequency Cepstrum Coefficient (Mfcc)  Minimum Average Entropy (Minae)  
Manifold Regularized Local Sparse Representation for Face Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2015, 卷号: 25, 期号: 4, 页码: 651-659
作者:  Wang, Lingfeng;  Wu, Huaiyu;  Pan, Chunhong
浏览  |  Adobe PDF(2100Kb)  |  收藏  |  浏览/下载:316/88  |  提交时间:2015/09/21
Face Recognition  Manifold Regularization  Sparse Representation  
BB-Homography: Joint Binary Features and Bipartite Graph Matching for Homography Estimation 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2015, 卷号: 25, 期号: 2, 页码: 239-250
作者:  Liu, Shaoguo;  Wang, Haibo;  Wei, Yiyi;  Pan, Chunhong
浏览  |  Adobe PDF(5043Kb)  |  收藏  |  浏览/下载:236/50  |  提交时间:2015/09/18
Bb-homography  Binary Feature Descriptor  Graph Matching (Gm)  Homography  Sparse Spectral Gm  
Edge-Directed Single-Image Super-Resolution via Adaptive Gradient Magnitude Self-Interpolation 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2013, 卷号: 23, 期号: 8, 页码: 1289-1299
作者:  Wang, Lingfeng;  Xiang, Shiming;  Meng, Gaofeng;  Wu, Huaiyu;  Pan, Chunhong
浏览  |  Adobe PDF(1243Kb)  |  收藏  |  浏览/下载:486/180  |  提交时间:2015/08/12
Edge-directed  Gradient Magnitude Transformation  Super-resolution