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- Publisher Website: 10.1109/TPAMI.2011.282
- Scopus: eid_2-s2.0-84866665730
- PMID: 22213763
- WOS: WOS:000308755000014
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Article: 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 | |
Keywords | Batch image alignment low-rank matrix occlusion and corruption robust principal component analysis sparse errors |
Issue Date | 2012 |
Citation | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, v. 34, n. 11, p. 2233-2246 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\ell-1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments on both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions. © 2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/326904 |
ISSN | 2023 Impact Factor: 20.8 2023 SCImago Journal Rankings: 6.158 |
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 | 2023-03-31T05:27:23Z | - |
dc.date.available | 2023-03-31T05:27:23Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, v. 34, n. 11, p. 2233-2246 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326904 | - |
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\ell-1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments on both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions. © 2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Pattern Analysis and Machine Intelligence | - |
dc.subject | Batch image alignment | - |
dc.subject | low-rank matrix | - |
dc.subject | occlusion and corruption | - |
dc.subject | robust principal component analysis | - |
dc.subject | sparse errors | - |
dc.title | RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPAMI.2011.282 | - |
dc.identifier.pmid | 22213763 | - |
dc.identifier.scopus | eid_2-s2.0-84866665730 | - |
dc.identifier.volume | 34 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 2233 | - |
dc.identifier.epage | 2246 | - |
dc.identifier.isi | WOS:000308755000014 | - |