【正文】 produce interruption as a result of existing noise and image dark. Thus edge detection contains the following two parts: 1) Using edge operators the edge points set are extracted. 2) Some edge points in the edge points set are removed and a number of edge points are filled in the edge points set. Then the obtained edge points are connected to be a line. The mon used operators are the Differential, Log, Canny operators and Binary morphology, etc. A. Differential operator Differential operator can outstand grey change. There are some points where grey change is bigger. And the value calculated in those points is higher applying derivative operator. So these differential values may be regarded as relevant ‘edge intensity’ and gather the points set of the edge through setting thresholds for these differential values. First derivative is the simplest differential coefficient. Suppose that the image is (, )f xy, and its derivative operator is the first order partial derivative /, /f xf y????. They represent the rateofchange that the gray f is in the direction of x and y . Yet the gray rate of change in the direction ofα is shown in the equation (1): cos sinff fxyααα?? ?=+?? ? (1) Under consecutive circumstances, the differential of the function isf fdf dx dyxy??=+??. The direction derivative of function ( , )f xy has a maximum at a certain point. And the direction of this point is arctan[ / ]f fyx????. The maximum of direction derivative is 22(/) (/)f xfy?? +?? . The vector with this direction and modulus is called as the gradient of the function f , that is, 39。 9781424425037/08/$ 169。AbstractEdge detection is a basic and important subject in puter vision and image processing. In this paper we discuss several digital image processing techniques applied in edge feature extraction. Firstly, wavelet transform is used to remove noises from the image collected. Secondly, some edge detection operators such as Differential edge detection, Log edge detection, Canny edge detection and Binary morphology are analyzed. And then according to the simulation results, the advantages and disadvantages of these edge detection operators are pared. It is shown that the Binary morphology operator can obtain better edge feature. Finally, in order to gain clear and integral image profile, the method of bordering closed is given. After experimentation, edge detection method proposed in this paper is feasible. Index TermsEdge detection, digital image processing, operator, wavelet analysis I. INTRODUCTION The edge is a set of those pixels whose grey have step change and rooftop change, and it exists between object and background, object and object, region and region, and between element and element. Edge always indwells in two neighboring areas having different grey level. It is the result of grey level being discontinuous. Edge detection is a kind of method of image segmentation based on range noncontinuity. Image edge detection is one of the basal contents in the image processing and analysis, and also is a kind of issues which are unable to be resolved pletely so far [1]. When image is acquired, the factors such as the projection, mix, aberrance and noise are produced. These factors bring on image feature’s blur and distortion, consequently it is very difficult to extract image feature. Moreover, due to such factors it is also difficult to detect edge. The method of image edge and outline characteristic39。 :22222f ffxy??Δ= +?? (11) The Log operator is the process of filtering and counting differential coefficient for the image. It determines the zero overlapping position of filter output using convolution of revolving sy