计算机工程 ›› 2010, Vol. 36 ›› Issue (24): 211-213.doi: 10.3969/j.issn.1000-3428.2010.24.076

• 图形图像处理 • 上一篇    下一篇

基于Type-2模糊聚类的图像分割算法

周晚辉,刘文萍   

  1. (北京林业大学信息学院,北京 100083)
  • 出版日期:2010-12-20 发布日期:2010-12-14
  • 作者简介:周晚辉(1983-),女,硕士研究生,主研方向:数字图像处理;刘文萍(通讯作者),副教授、博士
  • 基金项目:

    国家“973”计划基金资助项目(2009CB421105);国家自然科学基金资助项目(40771141);北京林业大学科技创新计划基金资助项目(BLYX200936)

Image Segmentation Algorithm Based on Type-2 Fuzzy Clustering

ZHOU Wan-hui, LIU Wen-ping   

  1. (College of Information, Beijing Forestry University, Beijing 100083, China)
  • Online:2010-12-20 Published:2010-12-14

摘要:

模糊C均值算法是图像分割的常用方法,但该算法对噪声非常敏感。为此,提出一种新算法,在模糊C均值算法基础上引进Type-2模糊理论,以提高算法的分割准确性和鲁棒性。该算法对模糊C均值算法中每一个样本的隶属度进行分段线性拉伸,利用拉伸的结果作为一个新的隶属度函数,并用该函数对图像进行分割。实验结果表明,该算法准确性较高,且具有良好的抗噪能力。

关键词: 图像分析, 图像分割, 模糊聚类, 二型模糊, 隶属函数

Abstract:

The Fuzzy C-Means(FCM) algorithm is one of the most popular image segmentation methods, but the FCM is sensitive to noise. A new image segmentation algorithm is proposed aiming to improve the segmentation precision and robustness of the FCM algorithm by introducing the Type-2 fuzzy theory. A piecewise-linear stretching method is applied to the membership values for each pixel. These membership values are derived using the FCM algorithm. The result of stretching defines a new membership function, which is used for image segmentation. Experimental results show the algorithm has higher image segmentation accuracy and better noise immune ability.

Key words: image analysis, image segmentation, fuzzy clustering, Type-2 fuzzy, membership function

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