Fast Multilevel Thresholding Method for Image Segmentation Based on Improved Particle Swarm Optimization and Maximal Variance

摘要：

　　To determine the optimal thresholds in image segmentation,a new multilevel thresholding method based on improved particle swarm optimization (IPSO) is proposed in this paper.Firstly,use the conception of independent peaks to divide the histogram to several regions,secondly,the optimization object function using maximum between-class variance (MV) method can be gotten in each area,by the non-uniform mutation and Geese-LDW PSO optimization of the object function,the optimal thresholds can be gotten,and the image can be segmented with the thresholds.Compared with the basic MV algorithm and genetic algorithm (GA) modified MV,the experimental results show that the new method not only realizes the image segmentation well,but also improves the speed.

关键词： Image segmentation PSO Non-uniform mutation Linear descend inertia weight Independent peaks Multilevel threshold Maximal variance

作者: Zhengtao PENG Kangling FANG Zhiqi SU Shihong Li

作者单位: Division of Process Control and Information TechnologyWuhan University of Science and Technology Wuhan, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1741-1746

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）