外文翻譯----基于數字圖像處理技術的邊緣特征提?。ㄓ⑽模?/h1>

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1、 978-1-4244-2503-7/08/$20.00 © 2008 IEEE Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, China September 2008 Edge Feature Extraction Based on Digital Image Processing Techn

2、iques Feng-ying Cui and Li-jun Zou Bei Song College of Automation and Electrical Engineering College of Automation and Electrical Engineering Qingdao University of Science and Technology, Qingdao University of Science an

3、d Technology, Qingdao 266042, China Qingdao, ShanDong Province, China{cfy_angel & zlj105983}@163.com pipisongbei@163.com ?Abstract-Edge detection is a basic and important subject in computer vision and image process

4、ing. 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

5、 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 edg

6、e 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.

7、After experimentation, edge detection method proposed 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 whos

8、e 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 dif

9、ferent 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 i

10、mage 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. The

11、se 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 outl

12、ine 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 disc

13、usses 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

14、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 op

15、erator. 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 con

16、tain 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 dif

17、ficulties 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 shor

18、tcoming 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 p

19、rocessing [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

20、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. Fig.

21、1 the sketch of 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 [3][4] as follows: 1) Image is preprocessed (suc

22、h as the gray-scale adjustment, etc.). 2) Wavelet multi-scale decomposition is adopted to process image. 3) In each scale, wavelet coefficients belonging to noises are removed and the wavelet coefficients are remain

23、ed 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 Fig. 2. 2320(2) Sobel and Prewitt operator To

24、 reduce the influence of noise when detecting edge, the Prewitt operator enlarges edge detection operator template from two by two to three by three to compute difference operator. Using the Prewitt operator can not o

25、nly detect edge points, but also restrain the noise. The Sobel operator counts difference using weighted for 4 neighborhoods on the basis of the Prewitt operator. The Sobel operator has the similar function as the Pr

26、ewitt operator, but the edge detected by the Sobel operator is wider. Suppose that the pixel number in the 3 3 × sub-domain of image is as follows: 0 A 1 A 2 A7 A ( , ) f i j 3 A6 A 5 A 4 AWe ord

27、er that 0 1 2 6 5 4 ( ) ( ) X A A A A A A = + + ? + +and 0 7 6 2 3 4 ( ) ( ) Y A A A A A A = + + ? + + . Then Prewitt operator is as follows: 2 2 1/ 2 [ ( , )] ( ) G f i j X Y = +(7)Or [ ( , )] G f i j X Y = +

28、(8) Prewitt operator is said in Fig.4 in the form of the template. Fig. 4 Prewitt operator Sobel operator can process those images with lots of noises and gray gradient well. We order that 0 1 2 6 5 4 ( 2 ) ( 2 ) X A A

29、 A A A A = + + ? + +and 0 7 6 2 3 4 ( 2 ) ( 2 ) Y A A A A A A = + + ? + + . Then Sobel operator is as follows: 2 2 1/ 2 [ ( , )] ( ) G f i j X Y = +(9) Or [ ( , )] G f i j X Y = +(10)The template of the Sobel o

30、perator is shown in Fig.5. Fig. 5 Sobel operator The original image of cable insulation layer and the edge detection drawing of Sobel operator gained using MatLab simulation are shown in Fig. 6 and Fig. 7. Fig. 6 the

31、original image Fig. 7 the edge detection drawing of Sobel operator From the simulation drawing Fig. 7, we can know that the edge position is very accurate. And the effect of Sobel edge detection is very satisfying. In

32、a word, the Sobel and Prewitt operators have a better effect for such images with grey level changing gradually and more noises. B. Log operator The Log operator is a linear and time-invariant operator. It detects e

33、dge points through searching for spots which two-order differential coefficient is zero in the image grey levels. For a continuous function ( , ) f x y , the Log operator is defined as at point ? x, y? :2 2 22 2f f f x

34、 y? ? 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 symmetr