SuperMatching: feature matching using supersymmetric geometric constraints

Cheng, Zhi-Quan, Chen, Yin ORCID: https://orcid.org/0000-0003-2976-4912, Martin, Ralph Robert, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Wang, Aiping 2013. SuperMatching: feature matching using supersymmetric geometric constraints. IEEE Transactions on Visualization and Computer Graphics 19 (11) , pp. 1885-1894. 10.1109/TVCG.2013.15

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Abstract

Feature matching is a challenging problem at the heart of numerous computer graphics and computer vision applications. We present the SuperMatching algorithm for finding correspondences between two sets of features. It does so by considering triples or higher order tuples of points, going beyond the pointwise and pairwise approaches typically used. SuperMatching is formulated using a supersymmetric tensor representing an affinity metric that takes into account feature similarity and geometric constraints between features: Feature matching is cast as a higher order graph matching problem. SuperMatching takes advantage of supersymmetry to devise an efficient sampling strategy to estimate the affinity tensor, as well as to store the estimated tensor compactly. Matching is performed by an efficient higher order power iteration approach that takes advantage of this compact representation. Experiments on both synthetic and real data show that SuperMatching provides more accurate feature matching than other state-of-the-art approaches for a wide range of 2D and 3D features, with competitive computational cost.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Feature matching; geometric constraints; supersymmetric tensor
Additional Information:	Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/1077-2626/ (accessed 30/10/14). © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher:	IEEE
ISSN:	1077-2626
Date of First Compliant Deposit:	30 March 2016
Last Modified:	21 Nov 2024 12:30
URI:	https://orca.cardiff.ac.uk/id/eprint/51468

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