An Edge Growing Approach for Segmentation of Grey and Color Images
Shi Qing · 2000
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.