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- Publisher Website: 10.1109/CVPR.2010.5540138
- Scopus: eid_2-s2.0-77956007151
- WOS: WOS:000287417500098
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Conference Paper: RASL: Robust Alignment by Sparse and Low-rank decomposition for linearly correlated images
Title | RASL: Robust Alignment by Sparse and Low-rank decomposition for linearly correlated images |
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Authors | |
Issue Date | 2010 |
Citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, p. 763-770 How to Cite? |
Abstract | This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of ℓ1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions. ©2010 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/326835 |
ISSN | 2023 SCImago Journal Rankings: 10.331 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Peng, Yigang | - |
dc.contributor.author | Ganesh, Arvind | - |
dc.contributor.author | Wright, John | - |
dc.contributor.author | Xu, Wenli | - |
dc.contributor.author | Ma, Yi | - |
dc.date.accessioned | 2023-03-31T05:26:52Z | - |
dc.date.available | 2023-03-31T05:26:52Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, p. 763-770 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326835 | - |
dc.description.abstract | This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of ℓ1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions. ©2010 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.title | RASL: Robust Alignment by Sparse and Low-rank decomposition for linearly correlated images | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CVPR.2010.5540138 | - |
dc.identifier.scopus | eid_2-s2.0-77956007151 | - |
dc.identifier.spage | 763 | - |
dc.identifier.epage | 770 | - |
dc.identifier.isi | WOS:000287417500098 | - |