基于快速模糊C均值聚类算法的红外图像分割

黄永林; 叶玉堂; 乔闹生; 陈镇龙

doi:doi:10.3788/hplpb20112306.1467

强激光与粒子束, 2011, 23 (6): 1467, 网络出版: 2011-07-04

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

论文大纲

黄永林 ^*叶玉堂乔闹生陈镇龙

作者单位

电子科技大学光电信息学院, 成都 610054

模糊C均值聚类图像分割区域生长红外图像模式识别 fuzzy C-means clustering image segmentation region growing infrared image pattern recognition

摘要

针对模糊C均值(FCM)聚类图像分割需要预先知道类别数及计算量较大的问题，提出了新的快速FCM改进方法。首先，利用边缘信息进行邻域搜索得到种子像素；通过区域生长快速获得区域分割类别数和对应的聚类中心值，并将图像分成确定类别的区域和未确定类别的区域；最后利用所得的聚类中心值和 FCM算法对未确定类别区域进行聚类。实验证明,本文提出的改进方法大大减少了计算量，显著提高了图像分割速度，而且由于聚类考虑了相邻像素点的关系，图像分割结果能够清晰地保留目标轮廓，提高了图像分割的质量。

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.

参考文献

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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. Huang Yonglin, Ye Yutang, Qiao Naosheng, Chen Zhenlong. Infrared image segmentation based on fast fuzzy C-means clustering[J]. High Power Laser and Particle Beams, 2011, 23(6): 1467.

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