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Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation | |
2014-10 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING (IF:10.8[JCR-2023],12.1[5-Year]) |
ISSN | 1057-7149 |
卷号 | 23期号:10页码:4527-4538 |
发表状态 | 已发表 |
DOI | 10.1109/TIP.2014.2352497 |
摘要 | Designing an appropriate regularizer is of great importance for accurate optical flow estimation. Recent works exploiting the nonlocal similarity and the sparsity of the motion field have led to promising flow estimation results. In this paper, we propose to unify these two powerful priors. To this end, we propose an effective flow regularization technique based on joint low-rank and sparse matrix recovery. By grouping similar flow patches into clusters, we effectively regularize the motion field by decomposing each set of similar flow patches into a low-rank component and a sparse component. For better enforcing the low-rank property, instead of using the convex nuclear norm, we use the log det (.) function as the surrogate of rank, which can also be efficiently minimized by iterative singular value thresholding. Experimental results on the Middlebury benchmark show that the performance of the proposed nonlocal sparse and low-rank regularization method is higher than (or comparable to) those of previous approaches that harness these same priors, and is competitive to current state-of-the-art methods. |
关键词 | Optical flow low-rank sparse representation nonlocal self-similarity |
URL | 查看原文 |
收录类别 | SCI ; EI |
语种 | 英语 |
资助项目 | Fundamental Research Funds of the Central Universities of China[BDY081424] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000342159100008 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
EI入藏号 | 20144100082984 |
EI主题词 | Benchmarking ; Iterative methods |
EI分类号 | Light/Optics:741.1 ; Industrial Engineering and Management:912 ; Production Planning and Control; Manufacturing:913 ; Numerical Methods:921.6 |
WOS关键词 | SEGMENTATION ; MINIMIZATION |
原始文献类型 | Article |
引用统计 | 正在获取...
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文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2370 |
专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_马毅组 |
通讯作者 | Dong, Weisheng |
作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230000, Peoples R China 3.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai 200444, Peoples R China 4.Univ Illinois, Dept Elect & Comp Engn, Champaign, IL 61820 USA |
推荐引用方式 GB/T 7714 | Dong, Weisheng,Shi, Guangming,Hu, Xiaocheng,et al. Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(10):4527-4538. |
APA | Dong, Weisheng,Shi, Guangming,Hu, Xiaocheng,&Ma, Yi.(2014).Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(10),4527-4538. |
MLA | Dong, Weisheng,et al."Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.10(2014):4527-4538. |
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