灰度和彩色图象分割的一种改进算法

An Improved Algorithm for Segmentation of Grey and Color Images

图象边缘检测技术是图象处理中最重要的内容之一,且已在图象分析和识别区域中得到广泛的应用。但由于边缘点不连续和难以把存在大量碎边缘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。为了提高图象分割的边缘准确性和区域一致性以及降低分割错误率,提出用边缘生长的方法来解决不连续的边缘点链接问题,以便间接地将高细节区围成一个区域。该算法也可以嵌入到其他利用边缘信息的分割算法中。

Edge detection is one of the most important techniques in image processing; it is often used in the field of image analysis and image recognition for many 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. To improve the accuracy of boundary locations and region homogeneity as well as to reduce the error rate in image segmentation, we present a novel approach called edge growing to attack edge discontinuity after edge point detection, therefore high detail areas enclosed by the adjacent salient regions can be indirectly extracted as a large areas. The algorithms can be embedded in other complicated segmentation procedures to incorporate edge information.