A New Image Segmentation Method Based on Region and Edge
一种新的基于区域和边界的图象分割方法

Image segmentation based on watershed method always results in over segmentation and makes the contours of the objects buried in the irrelevant watershed lines. In this paper, we first smooth the image while preserving the edge by nonlinear diffusion method, which can reduce the noise in the image and at the same time the numbers of the region minimums of the gradient image that is equal to the region numbers of watershed segmentation. Then from the result of the initial watershed segmentation which is organized by the Region Adjacency Graph (RAG), we execute the bottom up hierarchical region merging according to the region average gray value similarity and the edge strength criterions that can settle the over segmentation problem well. The region average gray value is a rough characteristic about the region while the edge strength criterion is local. Similarity criterion of the average gray value of the region is first used in Merging operation. Then upcast the RAG and continue to merge according to edge strength. The results of the experiment using 2D real images show that this method can provide accurately localized and closed region contours.