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Edge Detection Based on Improved Sobel Operator

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Advances in Computer Science Research, (ACSR), volume 52 2016 International Conference on Computer Engineering and Information Systems (CEIS-16) Edge Detection Based on Improved Sobel Operator Chao-Chao Zhang, Jian-Dong Fang Inner Mongolia University of Technology Electric Engineering College, Hohhot 010080, China E-mail: [email protected], [email protected] Abstract-Sobel algorithm is an important method of image edge many respects, Sobel operators are superior to others. detection. Comparing the Sobel operator with several other However, the classical Sobel operator also has some edge detection operators used frequently and making a further problems. The Sobel operator is sensitive only to the study on the classical Sobel operator, the advantages of Sobel vertical direction and the horizontal direction. However, the operator are its fast detection speed, meanwhile , it has an image information is not limited to the horizontal and effect on smoothing and suppressing noise. Also, Sobel vertical direction; it can make part of the image information operator has a good effect on edge detection. Although Sobel lose. In this paper, a new improved operator is proposed operator has advantages in many aspects, it exists some based on the sobel operator. On the traditional Sobel problems: the Sobel operator is a kind of edge detection in operator, the 45 degrees and 135 degrees 2 direction horizontal and vertical direction, so it neglects edge points in other directions. It can not achieve a true detection for the templates are added to realize multi- directional image points on image edge. In this paper, the algorithm is based on acquisition. Then calculate the threshold by using the Otsu the Sobel operator, an increase of 45 degrees and 135 degrees 2 method and refine the detected rough edges by using direction template, while the main edge of the oblique, binarization method to achieve the results of image edge re-assigned the weight of the operator template. At the same detection. Edge detection effect can be achieved better by time, in order to achieve an effect of detection, binarization using Matlab simulation method. method is used to make an edge thinning for detected image. A comparison table of various operators is shown in According to simulation experiments, they show this method is Table 1: simple and feasible, and the detective result is more concrete and abundant than traditional Sobel edge detection. Some II. THE COMPARISON OF SEVERAL problems are improved, such as traditional Sobel edge is rough TRADITIONAL OPERATORS and detection is incomplete. • Roberts operator: did not pass smooth calculation, so it is more sensitive to the noise. Keywords-sobel operator; binarization; edge detection; edge thinning • Prewitt operator and Sobel operator: edge extraction effect is almost the same. Prewitt operator is an I. INTRODUCTION average filter, Sobel operator is a weighted average filter; they have better detection effect on images In digital image processing, edge feature is one of the which have low gray level noise, but the detection important features of the image, and it is an important part effect is not obvious. of image processing, pattern recognition and computer • vision. The results of image edge detection affect the further LOG operator: detecting edge by using two order image processing and pattern recognition directly. In recent derivatives zero crossing edge method. Smoothing decades, the image edge detection technology has become effect is more significant, removing effect of noise an important research topic in digital image processing is better, but the loss of image details is larger, the technology. With the development of science and edge precision is lower. So there is a contradiction technology, researchers have proposed some methods for between positioning edge accuracy and eliminating image edge detection and evaluation of edge detection. At noise level. the same time, these edge detection techniques are applied III. THE PRINCIPLES OF THE TRADITIONAL to the field of computer vision and pattern recognition, SOBEL OPERATOR which makes the application of edge detection technology more and more widely [1]. Over the years, image Sobel cross-differential operator, because of its role in segmentation has been attracting more and more attention. the template is small, the relative calculation is also small, Thousands of segmentation algorithms have been put thus the image details of the contour can be obtained. But forward. They can be divided into bit threshold method, because the template size is even, the pending pixel cannot edge detection method and regional growth method [2]. be placed in the center position of the template, or the Edge detection method includes: first order differential edge results will have half pixel fault. Sobel differential operator detection operator includes Roberts operator, Prewitt is a directional differential operator in a basis of odd size operator, LOG operator and Sobel operator.In contrast, in template. The expressions of formula as follow (3.1), (3.2): Copyright © 2016, the Authors. Published by Atlantis Press. 129 This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, (ACSR), volume 52 G( i , j )= f[][] i − 1, j + 1 + 2 × f i , j + 1 idea of Sobel operator algorithm: because the edge of the x image is located at the place in which the brightness +f[][] i +1, j + 1 − f i − 1, j − 1 (3.1) changes significantly, therefore in the neighborhood of the pixel gray value of pixels exceeds a set threshold depending −2 ×f[][] i , j − 1 − f i + 1, j − 1 on the specific steps for the edge [3]. The specific steps of Sobel operator algorithm are as follows [4]: = + − + × + • Moving the horizontal and vertical direction Gy () i, j f[][] i 1, j 1 2 f i 1, j templates from left to right, from top to bottom, and moving from one pixel to another, and the center of (3.2) +f[][] i +1, j + 1 − f i − 1, j − 1 the template corresponds to the corresponding −2 ×f[][] i − 1, j − f i − 1, j + 1 pixels in the image. • Multiplying the coefficients in the template with the corresponding pixel values in the image. The convolution template of the Sobel operator is • The gradient value is calculated as a new gray value expressed as the formula (3.3) and (3.4): by using 2 convolution values. • Select the appropriate threshold TH, if the new pixel −1 − 2 − 1 value ≥ TH, the pixel points can be regarded as = image edge points. Gx 0 0 0 (3.3) 1 2 1 IV. THE IMPROVEMENT OF THE TRADITIONAL SOBEL OPERATOR −1 0 1 Adding 45 degrees and 135 degrees direction template in a basis of the traditional Sobel operator, the direction G = − 2 0 2 (3.4) template are changed into four directions: the horizontal and y vertical direction and 45 degrees and 135 degrees direction. −1 0 1 It improved the weights of new template in the direction of oblique edge. Specific details are as follows: The calculating steps of Sobel operator: first, the edge According to the calculation of the four template detection image is divided into matrix form (3.5): −1 − 2 − 1 - 2 -1 0 0 0 0 -1 0 1 1 2 1 0 1 2 f1()()() x−1, y − 1 f1 x − ,1 y f1 x−1, y + 1 horizontal direction 45 degree direction A = f1()()() x, y− 1 f1 x, y f1 x, y + 1 (3.5) −1 0 1 0 1 2 f1()()() x+1, y − 1 f1 x + ,1 y f1 x+1, y + 1 − 2 0 2 -1 0 1 Multiply horizontal direction by vertical direction of the −1 0 1 - 2 -1 0 template and then multiply the vertical direction by vertical direction 135 degree direction horizontal direction of the template, Fx=Gx.*A, Fx=Gy.*A. directions and the calculating an image point by point, the gradient size calculation, as shown in the formula (3.6): maximum value is regarded as the pixel gray values. According to the threshold setting, edge point is determined. 2 2 The template maximum value corresponding to represents GG= +G (3.6) the direction of edge direction of the pixel. x y A. Edge Thinning Processing The formula for calculating the gradient direction is shown The edge of the image contains a lot of image in the formula (3.7): information, by the direction of the traditional Sobel operator improvement, only the gradient information. The G edge appears where the gradient value is large. To extract y θ = arctan (3.7) the edge of the image, a threshold must be set and then Gx binarized. In general, the gradient image obtained by the gradient operator has a thicker edge. If the gradient image When θ is equal to zero, there is a vertical edge on threshold is set directly to binarization, it is difficult to find behalf of the image, the left side is dark er than right side. a suitable threshold, so that the detected edge can meet the The Sobel operator introduced the weighted local requirements. This is not easy to edge processing and edge average, it can not only affect the image edge detection but feature extraction and other post-processing.Therefore, it is also suppress noise further, but the edge is wider. The basis necessary to refine the edge of the gradient before 130 Advances in Computer Science Research, (ACSR), volume 52 binarizing the image gradient [5]. separate Sobel operator for image edge detection, and then The refinement process is to find the pixel (a, b) Max a use the algorithm for image edge detection; make a maximum value in the neighborhood of (a, b), and set the comparison between them, Figure 1and4 shows the original, threshold locally according to Max (a, b).
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