基于最大类间方差的最大熵图像分割  

Maximum entropy image segmentation based on maximum interclass variance

作　　者：易三莉[1] 张桂芳 贺建峰[1] 李思洁 YI San-li;ZHANG Gui-fang;HE Jian-feng;LI Si-jie(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《计算机工程与科学》2018年第10期1874-1881,共8页Computer Engineering & Science

基　　金：国家自然科学基金(11265007);教育部回国人员科研启动基金(2010-1561);云南省人培基金(KKSY201203030)

摘　　要：最大熵分割算法对于目标与背景之间界限模糊的图像分割效果较好,但该算法对图像边缘的处理能力较差。最大类间方差分割算法对图像边缘的识别能力较强,但该算法对于目标和背景之间界限模糊的图像分割效果不好。针对上述问题,提出了一种基于最大类间方差的最大熵图像分割算法,该算法既能很好地对目标与背景之间界限模糊的图像进行分割,又能有效地识别图像的边缘。实验结果表明,本文所提算法对目标与背景之间界限模糊的图像的分割效果以及对图像边缘的识别能力均优于传统的最大类间方差算法和最大熵算法,且具有更好的有效性和鲁棒性。The maximum entropy segmentation algorithm is good at segmenting images with fuzzy boundary between the target and background,but cannot deal with the image's edge effectively.The maximum interclass variance segmentation algorithm can well identify image edges,however,it cannot accurately segment the image with fuzzy boundary between the target and background.In order to deal with these problems,we propose a maximum entropy image segmentation algorithm based on maximum interclass variance.The algorithm can both segment the image with fuzzy boundary between the target and background and identify the edge of the image effectively.Experimental results prove that the proposed algorithm is superior to the traditional maximum interclass variance algorithm and the maximum entropy algorithm for the image segmentation with fuzzy boundary between the target and background with strong edge recognition ability and better effectiveness and robustness.

关 键 词：最大类间方差 图像分割 最大熵 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]