基于分散微粒群算法的二维模糊最大熵图像分割  

2-D fuzzy maximum entropy method in image segmentation based on DPSO

作　　者：田杰[1] 曾建潮[1] 侯明冬[2] 

机构地区：[1]太原科技大学系统仿真与计算机应用研究所,太原030024 [2]山东劳动职业技术学院电气自动化系,济南250000

出　　处：《计算机工程与应用》2008年第29期188-190,共3页Computer Engineering and Applications

基　　金：国家自然科学基金No.60674104~~

摘　　要：该文研究了基于二维模糊信息熵的图像分割方法,针对二维模糊信息熵图像分割方法求取阈值时存在的计算复杂、时间长、实用性差等问题,提出了基于优化微粒群算法的二维最大熵图像分割方法。DPSO算法对图像的二维阈值空间进行全局搜索,并将搜索得到的二维熵最大值所对应的点灰度-区域灰度均值作为阈值进行图像分割。同时,为了避免该算法收敛到局部最优解的问题,在算法中引入了变异策略。通过实验显示了该算法在收敛性和计算效率上较QPSO在内其它优化算法具有更好的优越性。The 2-D fuzzy maximum entropy image segmentation method is studied in this paper,for the problems that the method is complex,time-consuming and lack of practicability during evaluating threshold,a 2-D fuzzy maximum entropy image segmentation method based on DPSO is presented.The proposed method searches the 2-D space of threshold using DPSO,and takes the gray scale value of pixel and the gray scale mean value of region corresponding to the 2-D maximum entropy value in the search space as the threshold for image segmentation.Furthermore,a mutation strategy is designed to avoid premature convergence.Sinmlation results show the good efficiency of DPSO to image segmentation.

关 键 词：图像分割 二维模糊最大熵 分散粒子群优化算法 粒子群优化算法 算子行为的微粒群优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]