基于灰色系统理论的图象边缘检测新算法

A Novel Algorithm of Image Edge Detection Based on Gray System Theory

边缘检测是图象特征提取与分析理解的基础,其检测质量直接决定后期理解的效果.寻找一种对噪声不敏感、定位精确、不漏检真边缘又不引入假边缘的检测方法,一直是人们的努力目标.本文尝试与探讨了基于灰色系统理论的图象边缘检测新算法.该算法首先对图象基元的特点进行分析,以确定非边缘点参考序列和待比较序列,然后通过两个序列之间的灰色关联度区分边缘点和非边缘点.实验结果证明,该算法不仅能够较为准确地检测出有用的边缘信息,具有一定的抗噪声能力,而且还可通过调整关联度阈值控制边缘信息量,因此其是一种有效的、具有可调功能的边缘检测新算法.

Edge detection is the base of feature extraction, analysis and comprehension of image, the quality of which determines the results of subsequent processing. Thus it is an effort goal for people to find a kind of method that is insensitive to noise, precisely locates true edges and excludes false edges. This paper discusses and tests a novel algorithm of edge detection based on gray system theory. At first the characteristics of pixels are analyzed to decide non-edge referential sequence and the sequence to be compared, and then gray correlation degree of both sequences is employed to distinguish between non-edge pixels and edge pixels. Finally, the simulations prove that in binary image cases, the algorithm can obtain more precise edge pixels than some traditional edge detection algorithms, such as canny, log, sobel and zerocross, and tolerate some noise such as speckle, salt and gaussian. In grayscale image cases, it is unnecessary to change the referential sequence. Furthermore the quantity of edges can be controlled by adjusting the correlation degree threshold. So the technique is a new effective adjustable method of edge detection.