Infrared image segmentation based on two-dimensional maximum fuzzy entropy with genetic algorithm

2005 ◽  
Author(s):  
Jin Wu ◽  
Juan Li ◽  
Ya Qiu ◽  
Jian Liu ◽  
Jinwen Tian
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Infrared Image ◽  
Fuzzy Entropy ◽  
Two Dimensional ◽  
Infrared Image Segmentation

Genetic Algorithm Combined with Mutual Information for Image Segmentation

2010 ◽  
Vol 108-111 ◽  
pp. 1193-1198 ◽  
Author(s):  
De Jia Shi ◽  
Zhi Qiang Liu ◽  
Jing He
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Mutual Information ◽  
Infrared Image ◽  
New Method ◽  
Optimal Threshold ◽  
Improved Genetic Algorithm ◽  
Optimal Thresholds ◽  
Infrared Image Segmentation

In order to automatically determine the optimal threshold in image segmentation, this paper presented a new method of image segmentation based on improved genetic algorithm combined with mutual information; it used this improved genetic algorithm to globally optimize infrared image segmentation functions. This method could automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population, and kept the variety of population for rapidly converging to get the optimal thresholds in image segmentation, it overcame the shortcomings including worse convergent speed, easy to premature that exist in traditional genetic algorithm etc.

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