A Novel Segmentation Algorithm Based on Bare Bones Particle Swarm Optimization and Wavelet Mutation

作     者：ZHANG Wei 1,ZHANG Yuzhu 2 1.School of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266042,P.R.China2.Department of Electrical Engineering and Automation,Luoyang Institute of Science and Technology,Luoyang 471023,P.R.China 

会议名称：《第二十九届中国控制会议》

会议日期：2010年

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by Shandong Province Natural Science Foundation Y2008G14 

关 键 词：Image Segmentation Threshold Segmentation Bare Bones Particle Swarm Optimization Fuzzy Entropy Wavelet Mutation 

摘      要：Image segmentation is a difficult and challenging problem in the image *** bones particle swarm optimization(BBPSO)can not get good optimization performance because it easy to get stuck into local *** wavelet mutation when no fitness improvement is observed,a new segmentation algorithm based on wavelet mutation BBPSO(WMBBPSO)and fuzzy entropy is *** proposed algorithm uses WMBBPSO to explore fuzzy parameters of maximum fuzzy entropy,and to get the optimum fuzzy parameter combination,then obtain the segmentation *** to experiment results of the new algorithm compare with other two algorithms,the proposed algorithm performs good segmentation performance and low time *** can be use to real time and precision measure coal dust image.