Title: Maximum Entropy Method of Image Segmentation Based on Genetic Algorithm
Abstract:The traditional entropy threshold has shortcomings of theory and computational complexity,resulting in time-consuming in image segmentation and low efficiency.In order to improve the efficiency and ac...The traditional entropy threshold has shortcomings of theory and computational complexity,resulting in time-consuming in image segmentation and low efficiency.In order to improve the efficiency and accuracy of image segmentation,an image segmentation method is proposed,which combines the improved genetic algorithm with maximum entropy algorithm.First,the two-dimensional histogram based on the image gray value information is used to extract features,then three genetic operations of selecting,crossover and mutation are used to search for the optimal threshold for image segmentation.Simulation results show that the improved algorithm,compared with the traditional maximum entropy image segmentation algorithm,increases segmentation efficiency,and the accuracy of image segmentation has greatly improved,which speeds up the segmentation speed.Read More
Publication Year: 2011
Publication Date: 2011-01-01
Language: en
Type: article
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Cited By Count: 21
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