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Versatile spectral methods for point set matching |
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| Authors: | Alberto Silletti Alessandro Abate Jeffrey D. Axelrod |
| Affiliation: | a Department of Information Engineering, University of Padova, Padova, Italy b Delft Center for Systems and Control, TU Delft - Delft University of Technology, Delft, The Netherlands c Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA d Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA |
| 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. |
| Keywords: | Point set matching Transformations and correspondences Registration Similarity metrics Spectral methods Kernel methods |
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