Three-Dimension maximum between-cluster variance image segmentation method based on chaotic optimization

Authors:
Jiu-Lun Fan;Xue-Feng Zhang;Feng Zhao
Affiliations:
Department of Information and Control, Xi’an Institute of Post and Telecommunications, Xi’an, Shaanxi, P.R. China;Department of Information and Control, Xi’an Institute of Post and Telecommunications, Xi’an, Shaanxi, P.R. China;Department of Information and Control, Xi’an Institute of Post and Telecommunications, Xi’an, Shaanxi, P.R. China
Venue:
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
Year:
2006

Citing 4
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Abstract

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.