Maximum Variance Image Segmentation Based on Improved Genetic Algorithm

2007 ◽  
Author(s):  
Chun-mei Wang ◽  
Su-zhen Wang ◽  
Chong-ming Zhang ◽  
Jun-zhong Zou
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Improved Genetic Algorithm ◽  
Maximum Variance

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.

Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 925-928 ◽  
Author(s):  
Zhi Bo Xu ◽  
Pei Jiang Chen ◽  
Shi Li Yan ◽  
Tai Hua Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Image Process ◽  
Segmentation Method ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Maximum Variance ◽  
Segmentation Image ◽  
Selection Of

Threshold segmentation method was widely applied in image process and the selection of threshold affected the final results of image segmentation to a large extent. In order to improve the accuracy and the calculation speed of image segmentation, an Otsu threshold segmentation method based on genetic algorithm was offered. According to the threshold and the gray scale values of pixels, the pixels were divided into two categories, and then the genetic algorithm was used to find the maximum variance between clusters and obtain the optimal threshold of segmentation image. The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step.

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.

Test Methods Research of Cracks in Ceramics Based on Genetic Algorithm

2014 ◽  
Vol 977 ◽  
pp. 25-29
Author(s):  
Bing Xiang Liu ◽  
Feng Qin Wang ◽  
Xu Dong Wu ◽  
Ying Xi Li
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Optimization Problem ◽  
Fitness Function ◽  
Recognition Rate ◽  
Test Methods ◽  
Segmentation Method ◽  
Maximum Variance ◽  
Best Value ◽  
Crossover And Mutation

In order to improve the reliability of cracks in ceramics test, this paper puts forward a target adaptive segmentation method used by genetic algorithm and maximum-variance algorithm in all classes. This proposed method makes some appropriate improvements about crossover and mutation in genetic algorithm. Besides, the fitness function draws merits of maximum-variance algorithm in all classes and turns the best value in image segmentation into corresponding optimization problem. The simulation results of experiment shows the method proposed shortens the searching time and strengthens anti-noise property during image segmentation and improves recognition rate of cracks in ceramics.

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