Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-33765-9_20
Title: In defence of RANSAC for outlier rejection in deformable registration
Authors: Tran, Q.-H.
Chin, T.-J.
Carneiro, G.
Brown, M.S. 
Suter, D.
Issue Date: 2012
Source: Tran, Q.-H.,Chin, T.-J.,Carneiro, G.,Brown, M.S.,Suter, D. (2012). In defence of RANSAC for outlier rejection in deformable registration. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7575 LNCS (PART 4) : 274-287. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-33765-9_20
Abstract: This paper concerns the robust estimation of non-rigid deformations from feature correspondences. We advance the surprising view that for many realistic physical deformations, the error of the mismatches (outliers) usually dwarfs the effects of the curvature of the manifold on which the correct matches (inliers) lie, to the extent that one can tightly enclose the manifold within the error bounds of a low-dimensional hyperplane for accurate outlier rejection. This justifies a simple RANSAC-driven deformable registration technique that is at least as accurate as other methods based on the optimisation of fully deformable models. We support our ideas with comprehensive experiments on synthetic and real data typical of the deformations examined in the literature. © 2012 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40277
ISBN: 9783642337642
ISSN: 03029743
DOI: 10.1007/978-3-642-33765-9_20
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