强激光与粒子束2011，Vol.23Issue(6)：1467-1470,4.DOI:10.3788/HPLPB20112306.1467
基于快速模糊C均值聚类算法的红外图像分割
Infrared image segmentation based on fast fuzzy C-means clustering
摘要
Abstract
The fuzzy C-means (FCM) algorithm has many disadvantages such as number of clusters must be determined before FCM clustering is implemented and the algorithm needs an amount of calculation. In order to solve these problems, a novel method of fast FCM clustering is proposed. Seed pixels can be obtained by neighborhood searching of edge information firstly; Number of clusters and the value of cluster centers can be achieved by region growing method. Image is separated into cluster regions and undetermined cluster regions. The value of cluster centers and FCM are adopted to determine the undetermined cluster regions. Experiences show that the new method greatly improved the efficiency of image segmentation. Since the relationship of neighbored pixels are taken into account, the results of image segmentation can maintain perfect and distinct targets contour and improved the quality of image segmentation.关键词
模糊C均值聚类/图像分割/区域生长/红外图像/模式识别Key words
fuzzy C-means clustering/image segmentation/region growing/infrared image/pattern recognition分类
信息技术与安全科学引用本文复制引用
黄永林,叶玉堂,乔闹生,陈镇龙..基于快速模糊C均值聚类算法的红外图像分割[J].强激光与粒子束,2011,23(6):1467-1470,4.基金项目
国防科技基础研究基金项目 （）
