一种基于边缘生长的灰度和彩色图象分割方法

边缘检测可以快速准确地提供区域分割的边缘点，是图象处理的一个重要领域。但由于边缘点不连续和难以把存在大量碎边缘点的高细节区提取出来这两个原因，而不能直接实现完整意义上的图象分割。为此提出用边缘生长的方法来解决不连续的边缘点链接问题和通过找出高细节区周围的区域，以便间接地将高细节区围成一个区域。该算法是边缘检测的后续处理，适合于多种应用目的，同时还可以嵌入到其它利用边缘信息的分割算法中。

An Edge Growing Approach for Segmentation of Grey and Color Images

Edge detection efficiently and accurately indicates the boundary points of image areas, so it is popularly used in various computer vision applications. However edge detection alone is not a whole image segmentation process, because usually the detected edges are not continuous and many loose edge points exist in high detail areas. In this paper we present a novel approach called edge growing to attack edge discontinuity after edge point detecion. Every salient edge point in an edge end would grow forward based on the edge structures in its neighborhood. All the edge end points grow simultaneously until closed edge contours are presented and the image is segmented into closed regions. After that, salient regions can be identified by its horizontal and vertical spans and extracted by contour tracking. Therefore high detail areas enclosed by the adjacent salient regions can be indirectly extracted as a large area, without grouping these high detail areas to use some complicated algorithms. As a procedures after edge detection, the algorithms can be applied in diverse applications, and can be embedded in other complicated segmentation procedures to incorporate edge information. Experimental results show that the algorithms proposed in this paper achieve excellent performance in color image segmentation.