基于云模型的图像区域分割方法

基于区域的图像分割方法由于其高效、稳健的特点成为自动或半自动图像分割方法的研究热点之一。针对区域分割方法中存在的不确定性问题，提出了一种基于云模型的区域分割方法。首先以云变换为基础确定了区域生长过程中的生长准则，然后以逆向云算法实现分割区域由定量的像素集合到定性的云概念的转换过程，最后以云综合算法为基础将相邻区域进行合并，实现了基于区域的不确定性图像分割。两组图像分割实验表明该方法可以准确地分割出目标，并优于传统的图像分割算法。

A New Method of Region Based Image Segmentation Based on Cloud Model

The region based segmentation method has been attracting much attention in automatic or semi-automatic image segmentation research. This paper proposes a new region based segmentation method based on cloud model in order to take uncertainty into account in image segmentation. Firstly, cloud transform algorithm is given to determine the growing criteria, and then the region is transformed from quantitative pixel set to qualitative concept by backward cloud algorithm, finally cloud synthesis algorithm is realized to merge the two adjacent regions. Experiments show that the new method can extract target from background accurately, and more effectively than traditional image segmentation method.