Image segmentation based on fuzzy 3-partition entropy approach and genetic algorithm

Authors:
Jin Wu;Juan Li;Jian Liu;Jinwen Tian
Affiliations:
College of Information Science & Engineering, Wuhan University of Science and Technology, Wuhan, P. R. China;College of Information Science & Engineering, Wuhan University of Science and Technology, Wuhan, P. R. China;Image Information & Intelligence Control Laboratory of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, P. R. China;Image Information & Intelligence Control Laboratory of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, P. R. China
Venue:
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
Year:
2004

Citing 3
Cited 0

A new approach for multilevel threshold selection

CVGIP: Graphical Models and Image Processing
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm

Pattern Recognition Letters

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Abstract

In this paper, a three-level thresholding method for image segmentation is presented, based on the concept of fuzzy c-partition and the maximum fuzzy entropy principle. A new fuzzy exponential entropy is defined through probability analysis. We also define simplified membership functions for the three parts respectively, while the fuzzy regions can be determined by maximizing fuzzy entropy. A genetic algorithm is implemented to search the optimal combination of the fuzzy parameters, which finally decide the thresholds. Experiments show that the proposed method can select the thresholds automatically and effectively, and the resulting image can preserve the main features of the components of the original image very well.