基于微粒群算法的二维最大熵图像分割算法  

2-D maximum entropy method of image segmentation based on PSO algorithm

作　　者：刘健辰[1] 沈洪远[1] 

机构地区：[1]湖南科技大学信息学院,湖南湘潭411201

出　　处：《计算机工程与应用》2007年第35期102-104,共3页Computer Engineering and Applications

基　　金：国家自然科学基金( the National Natural Science Foundation of China under Grant No.60375001);福建省自然科学基金( the Natural Science Foundation of Fujian Province of China under Grant No.2006J0017); 湖南省教育厅青年基金资助课题( No.05B016); 福建省青年科技人才创新项目( No.2005J048); 湖南省教育厅资助科研课题( the Research Project of Department of Education of Hunan Province; China under Grant No.B30534)

摘　　要：将微粒群算法运用于二维最大熵图像阈值分割法。首先构建图像分割的二维最大熵准则函数,然后采用适用于整数规划的微粒群算法最大化该准则函数,最终实现含噪声干扰下图像的有效分割。分割实验表明,该方法具有较强的抗噪声能力,且比普通和基于遗传算法的二维最大熵法运算速度更快。The PSO algorithm is used to optimize the 2-D maximum entropy for the image segmentation based on thresholds.The 2-D maximum entropy criteria function for image segmentation is buih.Then,the PSO algorithm is used to maximize the criterion function.Finally,the sample images with noise are segmented effectively by the proposed algorithm.Segmentation examples show that the method proposed has greater resistance capability to noise,and is faster than the common 2-D maximum entropy approach and the 2-D maximum entropy approach based on genetic algorithm.

关 键 词：图像分割 微粒群算法 熵 二维直方图 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] V233.4[自动化与计算机技术—计算机科学与技术]