A method for image edge detection based on threshold sectioning

Abstract

The invention proposes an image edge detection method based on threshold value segmentation, which is a new threshold value segmentation algorithm for taking threshold values ​​one by one for all points in the image and classifying them. Using the statistical information of the mean value of the gray value of the pixel neighborhood in the image as the threshold setting standard of the point, and introducing the variance of the gray value of the pixel in the neighborhood of the point as an additional judgment condition, so as to realize the global binarization that comprehensively considers the regional information, so that The extracted target points are the edges of the image. Moreover, the size of the structural elements for calculating the mean and variance can be adjusted, and structural elements of different sizes can be selected according to different needs. This method sets a threshold value for each pixel, so as to realize the global binarization comprehensively considering the area information, the image segmentation effect is good, and the edge location is accurate.

Description

technical field [0001] The invention belongs to the field of image processing, relates to an edge detection method, in particular to an image edge detection method based on threshold value segmentation. Background technique [0002] Image thresholding is a widely used image segmentation technique. This method first determines a gray threshold within the gray value range of the image, then compares the gray value of each pixel in the image with this threshold, and divides the corresponding pixels into two categories according to the comparison result: The gray value of the pixel is greater than the threshold for one class, and the gray value of the pixel is smaller than the threshold for the other class. These two types of pixels generally belong to the target and background areas in the image, so it is necessary to classify the pixels according to the threshold. Played the role of regional segmentation. [0003] The threshold-based segmentation is one of the most basic pro...

AI Technical Summary

Problems solved by technology

This method considers the difference in the distribution of regional gray values, and sets different thresholds for different blocks. To a certain extent, it solves the shortcomings of single-threshold segmentation, but it is prone to "block effect".

[0006] For an image containing complex information, it is difficult for the above methods to extract all the information in the image through one or several thresholds.

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