1、PDF外文：http:/ 3827字 外文资料 Edge Feature Extraction Based on Digital Image Processing Techniques AbstractEdge detection is a basic and important subject in computer vision and image processing. In this paper we discuss several digital image processing techniques applied in edge feature
2、 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 advant
3、ages and disadvantages of these edge detection operators are compared. 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 pro
4、posed in this paper is feasible. Index Terms-Edge 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,
5、and between clement and clement. 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 non-continuity. Image edge detection is one of the basal contents in
6、 the image processing and analysis, and also is a kind of issues which are unable to be resolved completely so far. 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 ve
7、ry difficult to extract image feature. Moreover, due to such factors it is also difficult to detect edge. The method of image edge and outline characteristic's detection and extraction has been research hot in the domain of image processing and analysis technique. Edge feature extraction has bee
8、n applied in many areas widely. This paper mainly discusses about advantages and disadvantages of several edge detection operators applied in the cable insulation parameter measurement. In order to gain more legible image outline, firstly the acquired image is filtered and denoised. In the process o
9、f denoising, wavelet transformation is used. And then different operators are applied to detect edge including Differential operator, Log operator, Canny operator and Binary morphology operator. Finally the edge pixels of image are connected using the method of bordering closed. Then a clear and com
10、plete image outline will be obtained. II. IMAGE DENOISING As we all know, the actual gathered images contain noises in the process of formation, transmission, reception and processing. Noises deteriorate the quality of the image. They make image blur. And many important features are covered up
11、.This brings lots of difficulties to the analysis. Therefore, the main purpose is to remove noises of the image in the stage of pretreatment. The traditional denoising method is the use of a low-pass or band-pass filter to denoise. Its shortcoming is that the signal is blurred when noises are remove
12、d. There is irreconcilable contradiction between removing noise and edge maintenance. Yet wavelet analysis has been proved to be a powerful tool for image processing. Because Wavelet denoising uses a different frequency band-pass filters on the signal filtering. It removes the coefficients of some s
13、cales which mainly reflect the noise frequency. Then the coefficient of every remaining scale is integrated for inverse transform, so that noise can be suppressed well. So wavelet analysis can be widely used in many aspects such as image compression, image denoising, etc. Fig. 1 the sketch of
14、removing image noises with wavelet transformation The basic process of denoising making use of wavelet transform is shown in Fig. 1, its main steps are as follows: 1) Image is preprocessed (such as the gray-scale adjustment, etc.). 2)Wavelet multi-scale decomposition is a
15、dopted to process image. 3)In each scale, wavelet coefficients belonging to noises are removed and the wavelet coefficients are remained and enhanced. 4)The enhanced image after denoising is gained using wavelet inverse transform. The simulation effect of wavelet denoising through Matlab is shown in
16、 Fig. 2. original image with image after median image after wavelet noise filtering denoising Fig. 2 the comparison of two denoising m
17、ethods Comparing with the traditional matched filter, the high-frequency components of image may not be destroyed using wavelet transform to denoise. In addition, there are many advantages such as the strong adaptive ability, calculating quickly, completely reconstructed, etc. So the signal to noise
18、 ratio of image can be improved effectively making use of wavelet transform. III. EDGE DETECTION The edge detection of digital image is quite important foundation in the field of image analysis including image division, identification of objective region and pick-up of region shape and so on.
19、Edge detection is very important in the digital image processing, because the edge is boundary of the target and the background. And only when obtaining the edge we can differentiate the target and the background. The basic idea of image detection is to outstand partial edge of the image making use
20、of edge enhancement operator firstly. Then we define the edge intensity' of pixels and extract the set of edge points through setting threshold. But the borderline detected may produce interruption as a result of existing noise and image dark. Thus edge detection contains the following two parts
21、: 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 are connected to be a line. The common used operators are the Differential, Log, Canny operators an
22、d 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 intensi
23、ty' 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(x,y) ,and its operator is the first order partial derivativef/ x,f/ y , .They represent the rate-of-change th
24、at the gray f is in the direction of x and y.Yet the gray rate of change in the direction of a is shown in the equation (1): f =fx cos +fy sin(1) Under consecutive circumstances,the differential of the function is df=fx dx +fy dy.The direction derivative of function f(x,y) has a maximum at a certain point. And the direction of this point is arctanfy / fx .The maximum of direction derivative is (fx)2 + (fy)2.The vector with this direction and modulus is called as the gradient of the function f， that is, f x, y = (fx , fx).So the gradient modulus operator is designed in the equation (2).
