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Feature matching using quasi-conformal maps

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Abstract

We present a fully automatic method for finding geometrically consistent correspondences while discarding outliers from the candidate point matches in two images. Given a set of candidate matches provided by scale-invariant feature transform (SIFT) descriptors, which may contain many outliers, our goal is to select a subset of these matches retaining much more geometric information constructed by a mapping searched in the space of all diffeomorphisms. This problem can be formulated as a constrained optimization involving both the Beltrami coefficient (BC) term and quasi-conformal map, and solved by an efficient iterative algorithm based on the variable splitting method. In each iteration, we solve two subproblems, namely a linear system and linearly constrained convex quadratic programming. Our algorithm is simple and robust to outliers. We show that our algorithm enables producing more correct correspondences experimentally compared with state-of-the-art approaches.

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Authors and Affiliations

  1. School of Mathematical Sciences, University of Science and Technology of China, Hefei, 230026, China

    Chun-xue Wang & Li-gang Liu

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  1. Chun-xue Wang
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  2. Li-gang Liu
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Correspondence to Li-gang Liu.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61672482 and 11626253) and the One Hundred Talent Project of the Chinese Academy of Sciences

ORCID: Chun-xue WANG, http://orcid.org/0000-0002-2118-3016

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Wang, Cx., Liu, Lg. Feature matching using quasi-conformal maps. Frontiers Inf Technol Electronic Eng 18, 644–657 (2017). https://doi.org/10.1631/FITEE.1500411

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  • DOI: https://doi.org/10.1631/FITEE.1500411

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