计算机工程与应用Issue(24)：157-160,4.DOI:10.3778/j.issn.1002-8331.1303-0510
马尔可夫随机场约束下的PCM图像分割算法
Image segmentation on Possibilistic C-Means clustering algorithm based on Markov spatial constraint
摘要
Abstract
Compared with Fuzzy C-Means(FCM)clustering, Possibilistic C-Means(PCM)has a better anti jamming capability. But the Possibilistic C-Means clustering is very sensitive to initial conditions and is very easy to cause the clustering result of consistency. And it doesn’t take into account the pixel spatial information. It is extremely unstable when it is used in image segmen-tation especially in multi-object image segmentation. Based on the PCM clustering, the prior spatial constraint is incorporated according to Markov random field theory, to build a new clustering objective function including the establishment of gray information and spatial information. This paper presents a new image segmentation algorithm(MPCM)combining Markov and PCM clustering. With experiments, using MPCM algorithm can achieve a better segmentation result than PCM in multi-object image segmentation.关键词
图像分割/可能性C均值/Markov随机场/聚类Key words
image segmentation/Possibilistic C-Means(PCM)/Markov random field/clustering分类
信息技术与安全科学引用本文复制引用
周彤彤,杨恢先,李淼,谭正华,张建波..马尔可夫随机场约束下的PCM图像分割算法[J].计算机工程与应用,2013,(24):157-160,4.基金项目
湖南省教育厅科研项目（No.10C1263）；湘潭大学科研项目（No.11QDZ11）。 （）
