Improved image edge detection method

Abstract

The invention discloses an improved image edge detection method. The method comprises the following steps of S1, using adaptive smooth filtering and smoothly inputting an image; S2, using the first order gradient template of a Sobel operator for reference and extending to the first order gradient templates in four directions of a horizontal direction, a vertical direction, a 45 degree and a 135 degree, carrying out convolution on the filtered image and acquiring first order gradient components at the four directions, and acquiring gradient amplitudes and gradient angles; and S3, using an Otsualgorithm to calculate a threshold, and combining a connection analysis method to determine the final edge of the image. In the invention, a traditional Canny edge detection algorithm is improved; firstly, the adaptive smooth filtering is used to replace Gaussian filtering in an original algorithm; then, the templates of the four directions of the horizontal direction, the vertical direction, the45 degree and the 135 degree are used to calculate a image gradient, noise sensitiveness is reduced and edge positioning precision is increased; after a non-maximal suppression step, the Otsu algorithm is introduced, and according to image gray level information, high and low thresholds are adaptively generated.

Description

technical field [0001] In particular, the present invention relates to an improved image edge detection method. Background technique [0002] The edge of the image refers to the set of pixels with a step change in the gray value of the surrounding pixels or a change in the roof, which reflects the discontinuity of the gray value of the image. The essence of edge detection is to use a certain algorithm to extract the boundary line between the target and the background in the image. Usually, the edge of the image can be obtained by detecting the maximum value of the first-order gradient or the zero-crossing point of the second-order derivative. Commonly used first-order gradient operators include Roberts, Prewitt, and Sobel; the most representative edge detection operator based on second-order derivative zero-crossing detection is the LoG operator proposed by Marr and Hildreth. These operators are all local window gradient operators. The advantage is that the amount of calcul...

AI Technical Summary

Problems solved by technology

However, the variance of the Gaussian function and the selection of high and low thresholds are all manually set, and the adaptive ability is poor; and it calculates the gradient using the finite difference mean in the 2×2 neighborhood, which is sensitive to noise