Canny edge detection algorithm based on local threshold

Abstract

The invention discloses a Canny edge detection algorithm based on a local threshold, and the algorithm mainly comprises the following steps: 1), carrying out the smooth filtering of an image, and removing noise through Gaussian filtering; 2) calculating an image gradient amplitude and direction based on the smoothed image; 3) performing non-maximum suppression calculation on the gradient directionto obtain an edge point, if the amplitude of the gradient of the current pixel point is greater than the gradient amplitude of two adjacent pixel points in the positive and negative directions of thegradient, regarding the point as the edge point, and marking the corresponding position of the point as 1, otherwise, suppressing the point as a non-edge point, and marking the corresponding positionof the point as 0; 4) obtaining a global proportion value; 5) calculating a local high-low threshold value matrix through an acceleration algorithm according to the global proportion value, and 6) detecting edges according to a double-threshold value matrix to obtain a final edge image. The Canny algorithm can detect the local significant edges in the image in a self-adaptive mode and has good acceleration calculation performance.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a Canny edge detection algorithm based on a local threshold. Background technique [0002] Edge is the most basic feature of an image. It can greatly reduce the information to be processed while retaining the shape information of the object. Therefore, edge detection is the most basic problem in the field of image processing. Its solution is crucial for feature extraction, description, and target recognition. Research has an important impact. The edge of a grayscale image refers to the most significant part of the grayscale change in the local area of ​​the image. The grayscale change of the image can be expressed by the gradient, and the first-order differential operator and the second-order differential operator are commonly used to describe the gradient. These operators are simple in algorithm and have better real-time performance, but they are mo...

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Problems solved by technology

[0004] 1) The threshold is set empirically, and the adaptive ability is poor; 2) Based on the global threshold, the difference in local threshold is not considered, and local obvious edges cannot be detected

For problem 1, the researchers proposed various methods to automatically calculate the adaptive high and low thresholds, such as using the maximum inter-class variance method, designing an automatic calculation formula based on the gray mean and variance mean of the image, and automatically calculating the high and low thresholds based on the gradient difference histogram; For problem 2, an adaptive edge detection method based on Canny theory [J] by Wang Zhi and He Saixian [J], in the Chinese Journal of Image and Graphics, 2004, 9(8): 957-962, the whole image is divided into For several sub-images, the edge gradient information of the sub-images is combined with the global edge gradient information to adaptively generate a dynamic threshold; Song Ying, Chen Ke, Lin Jiangli, etc.'s edge detection algorithm based on image segmentation [J], Computer Engineering, 2010 , 36(14): 196-197 In the literature, the image is divided into non-overlapping sub-blocks, and then the high and low thresholds of each sub-block are calculated, but there is a block effect problem in the edge connection between blocks; Zhang Fan, Peng Zhongwei, Meng Shuijin based on Improved Canny edge detection method with adaptive threshold [J], Computer Applications, 2012, 32(8): 2296-2298 In the literature, the image gradient variance is used as a criterion to divide the image into blocks, and then the maximum inter-class variance is used for each sub-block The method automatically obtains the high and low thresholds, and uses the interpolation method to solve the block effect, but the threshold value of the pixels in each sub-block is the same, there may still be inter-block effects for complex images, and different pictures need to set different sizes of judgment parameters K

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