Abstract
Edge 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 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 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 proposed in this paper is feasible.
Key Words- 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, 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 non-continuity. 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 completely 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 characteristic's detection and extraction has been research hot in the domain of image processing and analysis technique. Edge feature extraction has been 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 of 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 complete 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. 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 removed. There is irreconcilable contradiction between removing noise and edge maintenance. Yet wavelet analysis has been proved to be a powerful tool for image processing [2]. Because Wavelet denoising uses a different frequency band-pass filters on the signal filtering. It removes the coefficients of some scales 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 [3] [4], etc.

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