%0 Journal Article
%T Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization
%A L. DJEROU
%A N. KHELIL
%A N. H. DEHIMI
%A M. BATOUCHE
%J Journal of Applied Computer Science & Mathematics
%D 2012
%I Stefan cel Mare University of Suceava
%X In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu¡¯s, Kapur¡¯s and Kittler¡¯s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu¡¯s, Kapur¡¯s and Kittler¡¯s methods.
%K Binary Particle Swarm Optimization
%K Image Segmentation
%K Image Thresholding
%K Multi-objective Optimization
%K Non-pare To Approach
%U jacs.usv.ro/getpdf.php?paperid=13_4