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])
ISSN1057-7149
卷号23期号:10页码:4527-4538
发表状态已发表
DOI10.1109/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
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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
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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
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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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