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。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第24期211-213,216,共4页
Computer Engineering
基金
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)
关键词
图像分析
图像分割
模糊聚类
二型模糊
隶属函数
image analysis
image segmentation
fuzzy clustering
Type-2 fuzzy
membership function
