Research of improved genetic algorithm for thresholding image segmentation based on maximum entropy

2010 ◽  
Author(s):  
Jiang Hua Wei ◽  
Yang Kai
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Improved Genetic Algorithm

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for 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.

scholarly journals Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm

2015 ◽  
Vol 03 (11) ◽  
pp. 1-7 ◽  
Author(s):  
Liping Chen ◽  
Xiangyang Chen ◽  
Sile Wang ◽  
Wenzhu Yang ◽  
Sukui Lu
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy
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