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Framework for efficient optimal multilevel image thresholding

[+] Author Affiliations
Martin Luessi

Northwestern University, Department of Electrical Engineering and Computer Science, 2145 Sheridan Road, Evanston, Illinois 60208-3118

Marco Eichmann

University of Applied Sciences of Eastern Switzerland, Oberseestrasse 10, 8640 Rapperswil, Switzerland

Guido M. Schuster

University of Applied Sciences of Eastern Switzerland, Oberseestrasse 10, 8640 Rapperswil, Switzerland

Aggelos K. Katsaggelos

Northwestern University, Department of Electrical Engineering and Computer Science, 2145 Sheridan Road, Evanston, Illinois 60208-3118

J. Electron. Imaging. 18(1), 013004 (February 02, 2009). doi:10.1117/1.3073891
History: Received June 22, 2008; Revised November 10, 2008; Accepted December 03, 2008; Published February 02, 2009
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Image thresholding is a very common image processing operation, since almost all image processing schemes need some sort of separation of the pixels into different classes. In order to determine the thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. By defining two classes of objective functions for which the optimal thresholds can be found by efficient algorithms, this paper provides a framework for determining the solution approach for current and future multilevel thresholding algorithms. We show, for example, that the method proposed by Otsu and other well-known methods have objective functions belonging to these classes. By implementing the algorithms in ANSI C and comparing their execution times, we can also make quantitative statements about their performance.

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© 2009 SPIE and IS&T

Citation

Martin Luessi ; Marco Eichmann ; Guido M. Schuster and Aggelos K. Katsaggelos
"Framework for efficient optimal multilevel image thresholding", J. Electron. Imaging. 18(1), 013004 (February 02, 2009). ; http://dx.doi.org/10.1117/1.3073891


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