Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 34, Issue 11, Pages 2233-2246Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2011.282
Keywords
Batch image alignment; low-rank matrix; sparse errors; robust principal component analysis; occlusion and corruption
Funding
- US National Science Foundation (NSF) [IIS 08-49292, NSF ECCS 07-01676, NSF CCF 09-64215, ONR N00014-09-1-0230]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0964215] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1116012] Funding Source: National Science Foundation
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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 l(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.
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