Based On Rough Collection Theory Image Edge Examination Method Research

In image processing,as a basic characteristic,the edge of the image, which is widely used in the recognition, segmentation,intensification and compress of the image, is often applied to high-level domain.There are many kinds of ways to detect the edge. Anyway, there are two main techniques: one is classic method based on the gray grade of every pixel;the other one is based on wavelet and its multi-scale characteristic. The first method, which is got the longest research, get the edge according to the variety of the pixel gray. The main techniques are Robert, Laplace, Sobel, Canny and LOG algorithm.In recent years, a new kind of detection method, which based on the phase information of the pixel, is developed. We need hypothesize nothing about images in advance. The edge is easy to find in frequency domain. It's a reliable method.Rough sets theory, proposed by Professor Z.Pawlak in 1980s, is a theoretical method thatresearches not only vagueness and uncertainty of knowledge, but also expression and inductionof data. Before and after 1990, rough sets theory has gradually attracted worldwide attentionbecause of its successful applications in data decision-making and analysis, pattern recognition, data mining, machine learning and knowledge discovering. At present, image processingmethods based on rough sets theory have become the new investigative direction of imageprocessing. The main work of this paper is to explore some applications of rough sets theorythat includes image clustering, image edge-detecting and mathematical morphological. So far, though studies on this field have existed, but there's a lack of the reference literature and thebackground of application. This paper wants to do some work on it: one is to explore thepotential of rough sets theory in application; the other is to find a new image processingmethod.