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《Journal of Southwest University(Natural Science Edition)》 2009-01
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An Improved Genetic Algorithm for Image Segmentation Based on Maximum Interclass Variance

TAN Zhi-cun,LU Rui-hua School of Electronics and Information Engineering,Southwest University,Chongqing 400715,China  
After studying the traditional genetic algorithms for image segmentation based on the maximum interclass variance,an improved genetic algorithm,"double self-adaptive crossover probability" genetic algorithm,is proposed based on chromosomes and gene positions. The proposed algorithm makes full use of the histogram of the image to be segmentalized as the prior knowledge,thus causing the reduction of the primary population size and the enhancement of seeking priority of the genetic algorithm. The results of the experiments demonstrate that the proposed genetic algorithm in segmentation of an image is better than the traditional genetic algorithms.
【Fund】: 西南师范大学发展基金资助项目(SWNUF2004006);; 重庆市自然科学基金资助项目(2007BB2331)
【CateGory Index】: TP391.41
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