Three-Dimension Maximum Between-Cluster Variance Image Segmentation Method Based on Chaotic Optimization
Chaotic optimization is a new optimization technique. For image segmentation, conventional chaotic sequence is not very effective to three-dimension gray histogram. In order to solve this problem, a three-dimension chaotic sequence generating method is presented. Simulation results show that the generated sequence is pseudorandom and its distribution is approximately inside a sphere whose centre is (0.5 , 0.5 , 0.5). Based on this work, we use the proposed chaotic sequence to optimize three-dimension maximum between-variance image segmentation method. Experiments results show that our method has better segmentation effect and lower computation time than that of the original three-dimension maximum between-variance image segmentation method for mixed noise disturbed image.
KeywordsControl Point Image Segmentation Chaotic System Target Class Randomness Analysis
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