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Proceedings Paper

Combining multiple similarity metrics for corner matching
Author(s): Hatem Khater; Farzin Deravi
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Paper Abstract

Corner matching is an important operation in digital image processing and computer vision where it is used for a range of applications including stereo vision and image registration. A number of corner similarity metrics have been developed to facilitate matching, however, any individual metric has a limited effectiveness depending on the content of images to be registered and the different types of distortions that may be present. This paper explores combining corner similarity metrics to produce more effective measures for corner matching. In particular the combination of two similarity metrics is investigated using experiments on a number of images exhibiting different types of transformations and distortions. The results suggest that a linear combination of different similarity metrics may produce more accurate and robust assessments of corner similarity.

Paper Details

Date Published: 27 February 2007
PDF: 10 pages
Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 649704 (27 February 2007); doi: 10.1117/12.699034
Show Author Affiliations
Hatem Khater, Univ. of Kent, Canterbury (United Kingdom)
Farzin Deravi, Univ. of Kent, Canterbury (United Kingdom)

Published in SPIE Proceedings Vol. 6497:
Image Processing: Algorithms and Systems V
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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