文章基本信息

标题：Image Segmentation Using Automatic Selected Threshold Method Based on Improved Genetic Algorithm
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作者：Gong Kun Luo
期刊名称：Journal of Software Engineering
印刷版ISSN：1819-4311
电子版ISSN：2152-0941
出版年度：2014
卷号：8
期号：4
页码：399-408
DOI：10.3923/jse.2014.399.408
出版社：Academic Journals Inc., USA
摘要：In this study, an image segmentation using automatic selected threshold method based on improved genetic algorithm is presented. It can overcome the shortcomings of the existing image segmentation methods which only consider pixel gray value without considering spatial features and computational complexity of these algorithms is too large. Encoding, crossover, mutation operator and other parameters of genetic algorithm are improved moderately in this method. Optimal threshold for image segmentation is converted into an optimization problem in this new method. The selection algorithm is optimized by using simulated annealing temperature parameters to achieve selective pressures. In order to achieve image segmentation , the optimal threshold is solved by using optimizing efficiency of improved genetic algorithm. Simulation results show that the new algorithm greatly reduces the optimization time and enhances the anti-noise performance of image segmentation and improves the efficiency of image segmentation . Thus, the new method can facilitate subsequent processing for computer vision and can be applied to real-time image segmentation .