Abstract
In order to solve the shortcomings of traditional Sobel edge detection operator, such as low accuracy of image edge location and rough edge extracted, an improved edge detection algorithm based on Sobel operator is proposed. Firstly, in the aspect of the accuracy of edge detection, the direction template is increased from two directions of horizontal and vertical to eight directions; secondly, in terms of the rough edge extracted and the noise sensitivity, combined with the operation of mathematical morphology, the edge can be refined, and at the same time, some part of the noise can be suppressed. The experiment results show that the improved edge detection algorithm has a better accuracy in image positioning, and a certain ability to suppress noise. Finally, the detected image edge of the image is more delicate and clear than the traditional Sobel operator.
Highlights
In the 20th century, digital image processing technology began to appear and developed rapidly
Many new algorithms have been proposed in recent years, such as the edge detection algorithm based on neural network and morphology, etc [3,4]
Like the algorithm in the above formula, after obtaining the gradient value in each direction of the image, the new gray value and direction of the target pixel can be determined by judging the maximum value, and the image edge can be obtained by discriminating the edge point through the threshold value
Summary
In the 20th century, digital image processing technology began to appear and developed rapidly. The commonly used first-order differential operators are roberts operator, Sobel operator and Prewitt operator, and the second-order differential operators are Laplacian operator and LOG operator These traditional algorithms are simple in operation and fast in running, but they have defects such as many detected edge breakpoints, low positioning accuracy and sensitivity to noise. Many new algorithms have been proposed in recent years, such as the edge detection algorithm based on neural network and morphology, etc [3,4]. These algorithms have good detection effect, but the algorithm is complex and the implementation speed is slow. Compared with the traditional Sobel detection operator, it has better improvements in the thickness of the edge, positioning accuracy and so on
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