一种改进的模糊聚类算法在图像边缘检测中的应用

Application of an improved fuzzy clustering algorithm for image edge detection

提出了一种改进的模糊聚类图像边缘快速检测算法,该算法在利用像素灰度值的同时还考虑了像素的空间信息,基于模糊集合理论将图像从灰度空间映射成一个模糊隶属度矩阵,然后将隶属度矩阵中的元素作为样本进行模糊聚类,从而提取出图像边缘。基于热力学原理选取隶属度函数,通过调节温度系数,实现图像边缘由粗到细的提取。实验证明,该方法在计算速度、滤除噪声、提取边缘等方面均优于C-均值聚类算法。

An improved fuzzy clustering algorithm for edge detection is proposed, in which the gray levels and the relationship between neighborhood pixels have been disposed at the same time. Based on the theory of fuzzy set, the image is mapped from gray space to a fuzzy membership matrix, and then the members of fuzzy matrix are clustered through a fuzzy clustering algorithm, and the edge of image is extracted finally. The fuzzy membership is acquired based on the theory of thermodynamics, and the edge from rough to detail can be acquired by adjusting the temperature parameter. The experiment indicates that the method is better than C - means fuzzy clustering algorithm in the extraction edge, the rapidity of computation, and the filtration of noise.