一种基于粗集理论的图像分割方法

METHOD FOR IMAGE SEGMETNATION BASED ON ROUGH SETS

提出了一种基于粗集理论的图像分割方法.在图像聚类过程中的对象往往是具有相似关系而不是等价关系的对象.在本文中将相似关系应用到粗集理论中来解决图像中的聚类问题.由于噪声的干扰,往往会影响到图像分割的效果.本方法提出了边界点的最大隶属原则并进而提出了边界点的粗糙度以及边界点的最大隶属原则,从而大大减小了噪声的干扰.在此基础上给出了聚类质量的评价函数.该方法为进行图像分割提供了一个崭新的视角.

A method for image segmentation based on rough sets theory was presented. Objects in the clustering process often have similarity relation instead of equivalence relation. Rough sets theory was applied in similarity relation to solve clustering issue. In general, clustering results are easily corrupted by noises. In order to decrease noise disturbance, maximum membership principle of the boundary points and roughness are defined. Clustering evaluation function was presented on this base. The method provides us a new viewpoint on processing image.