Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 63, Issue 5, Pages 1115-1129Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2014.2388434
Keywords
Dense correspondence; L2E estimator; non-rigid; outlier; registration; regularization
Categories
Funding
- National Science Foundation [0917141]
- China Scholarship Council [201206160008]
- NSF CAREER award [IIS-0844566]
- NSF [IIS-1216528]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0917141, 1360568] Funding Source: National Science Foundation
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We introduce a new transformation estimation algorithm using the L2E estimator and apply it to non-rigid registration for building robust sparse and dense correspondences. In the sparse point case, our method iteratively recovers the point correspondence and estimates the transformation between two point sets. Feature descriptors such as shape context are used to establish rough correspondence. We then estimate the transformation using our robust algorithm. This enables us to deal with the noise and outliers which arise in the correspondence step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. In the dense point case for non-rigid image registration, our approach consists of matching both sparsely and densely sampled SIFT features, and it has particular advantages in handling significant scale changes and rotations. The experimental results show that our approach greatly outperforms state-of-the-art methods, particularly when the data contains severe outliers.
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