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Nonextensive information-theoretic measure for image edge detection

[+] Author Affiliations
A. Ben Hamza

Concordia University, Concordia Institute for Information Systems Engineering, 1455 de Maisonneuve Boulevard West, CB-410-8 Montréal, Québec, Canada

J. Electron. Imaging. 15(1), 013011 (February 24, 2006). doi:10.1117/1.2177638
History: Received March 27, 2005; Revised July 25, 2005; Accepted August 18, 2005; Published February 24, 2006
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We propose a nonextensive information-theoretic measure called Jensen-Tsallis divergence, which may be defined between any arbitrary number of probability distributions, and we analyze its main theoretical properties. Using the theory of majorization, we also derive its upper bounds performance. To gain further insight into the robustness and the application of the Jensen-Tsallis divergence measure in imaging, we provide some numerical experiments to show the power of this entopic measure in image edge detection.

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Citation

A. Ben Hamza
"Nonextensive information-theoretic measure for image edge detection", J. Electron. Imaging. 15(1), 013011 (February 24, 2006). ; http://dx.doi.org/10.1117/1.2177638


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