An Improved Sobel Edge Detection Operator
The paper proposes an improved gradient algorithm which is based on the existing sobel edge detection. The algorithm is not only simple, but also can solve the coordination problem between the detection precision and anti noise performance. Experimental results prove that using the modified gradient algorithm to detect image edge is not only clear outline, good real time, but also has better anti noise performance. Introduction The edge is the basic characteristics of the image, and which contains a wealth of internal information [1] The various detection methods in the existing do not have an absolute advantage; they also solve some problems at the same time corresponding with this or that deficiencies. For example, some algorithms have a strong anti noise performance, but the detection accuracy is not high [2] Contrary to improve detection accuracy and anti noise performance is getting worse. Although some will be both to certain extent coordinated, but the algorithm becomes complicated [3] it makes the results of the implementation time lengthened. In order to solve these contradictions, this paper puts forward an improved gradient algorithm, and verified the algorithm has the advantage in the anti noise performance through simulation experiments. The Traditional Sobel Edge Detection Operator The traditional sobel operator comprises two groups of 3x3 matrixes [4,5] which only have the horizontal and vertical directions of the template, but there are many directions of the edge. In order to detect the edge of the eight directions such as 45 135 etc, it was suggested that the improved sobel edge detection operator [6] which adds six directions of the template based on the traditional sobel algorithm, so it becomes eight directions of the template as shown in Fig. 1. Fig. 2 is schematic diagram of the direction. Although the sobel edge detection operator takes eight directions of the template image convolution, it increases the edge detection of direction [6,7] but due to increase the computational complexity, the speed of edge detection is reduced. Figure 1. Eight direction template 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016) Copyright (c) 2016, the Authors. Published by Atlantis Press. This is an open access article under the CC BY NC license (http:/creativecommons.org/licenses/by nc/4.0/ Advances in Intelligent Systems Research, volume 130