文献类型：期刊文献

中文题名：基于遗传模糊C-均值与概率松弛法的图像分割研究

英文题名：Image Segmentation Based on GA-FCM Clustering and Probability Relaxation

作者：朱煜[1];江林佳[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2008

卷号：38

期号：4

起止页码：392

中文期刊名：激光与红外

外文期刊名：Laser & Infrared

收录：CSTPCD;;Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金项目(No.50403005);华东理工大学青年骨干教师项目(No.0156101)资助

语种：中文

中文关键词：图像分割;模糊C-均值;遗传算法;概率松弛;目标提取

外文关键词：image segmentation ; fuzzy C-means; genetic algorithm; probability relaxation; object extraction

摘要：在利用遗传模糊C-均值对图像像素进行初步分类的基础上,采用概率松弛算法对目标与背景间的疑似像素进行进一步分割和目标提取,很好地解决了目标提取不完整的问题,实验结果表明,该算法具有良好的特性。
Image segmentation is an essential approach for image processing. For some complex images, the extracted objects usually have a problem of incomplete edge or broken boundary. To solve this problem, we first apply fuzzy Cmeans clustering method based on genetic algorithm （GA-FCM） to segment the image pixels into different regiments. Besides background pixels and definite object pixels, union operation shows that the pixels around broken area are uncertain to be classified into object or background. We present probability relaxation （PR） algorithm to further segment the uncertain pixels according to their statistic properties. Experimental results indicate that this algorithm well solved the above problem and is effective for image segmentation and object extraction.