Versatile spectral methods for point set matching

Authors:
Alberto Silletti;Alessandro Abate;Jeffrey D. Axelrod;Claire J. Tomlin
Affiliations:
Department of Information Engineering, University of Padova, Padova, Italy;Delft Center for Systems and Control, TU Delft - Delft University of Technology, Delft, The Netherlands and Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA;Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA;Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

This work is concerned with the problem of point set matching over features extracted from images. A novel approach to the problem is proposed which leverages different techniques from the literature. It combines a number of similarity metrics that quantify measures of correspondence between the two sets of features and introduces a non-iterative algorithm for feature matching based on spectral methods. The flexibility of the technique allows its straightforward application in a number of diverse scenarios, thus overcoming domain-specific limitations of known techniques. The proposed approach is tested in a number of heterogeneous case studies: of synthetic nature; drawn from experimental biological data; and taken from known benchmarks in computer vision.