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

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.