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7. A multilevel thresholding method for image segmentation based on multiobjective particle swarm optimization

Author

Ameur Mustapha, Jabrane Younes, and Habba Maryam

Subjects
business.industry, Particle swarm optimization, Scale-space segmentation, Pattern recognition, 02 engineering and technology, Image segmentation, 01 natural sciences, Multi-objective optimization, Thresholding, 010309 optics, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Entropy (information theory), 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, Multi-swarm optimization, business, Mathematics
Abstract

In this paper, a multilevel thresholding method for image segmentation based on multiobjective particle swarm optimization (MOPSO) is proposed. The best thresholds determination process is seen as an optimization (minimization) problem which is solved by MOPSO. Thus, the goal is to find the optimal values of thresholds (the number of thresholds is given by the user) that give the best result of segmentation for targeted image. Two quantitative functions based on the entropy calculation of the regions are used as the fitness functions of the MOPSO. The proposed method has been tested on different images (filtered by a low pass filter) and the experimental results show its efficiency.

Published
2017

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