Edge detection method of Canny operator based on high and low thresholds

Abstract

The invention provides an edge detection method of a Canny operator based on high and low thresholds, which mainly comprises the following steps of: 1) carrying out smooth filtering processing on a source image, and removing noise of the image by using switch median filtering instead of Gaussian filtering; (2) calculating the gradient magnitude and direction of the image after smoothing filtering and denoising in the step (1) by adopting a sobel operator; 3) performing non-maximum suppression on the gradient magnitude obtained in the step 2) to obtain an edge image with a single-pixel width; 4) adopting a k-means clustering algorithm to obtain clustering centers of high and low gradient values; 5) obtaining an otsu threshold value of the gradient through an otsu algorithm, and taking a high threshold value and a low threshold value among the high clustering center, the otsu threshold value and the low clustering center; 6) processing the edge image of the single pixel width obtained in the step 3 by using a high threshold value and a low threshold value, and obtaining a binary edge; and 7) removing the interference edge of the binarized edge by adopting an area morphological opening operation, and obtaining a final edge image. The Canny algorithm provided by the invention has the advantages of high positioning precision, strong adaptability, good interference point removal effect and the like.

Description

technical field [0001] The invention relates to the field of digital image processing, and specifically provides an edge detection method based on a Canny operator with high and low thresholds. Background technique [0002] The edge feature is the basic feature of the image, it can greatly reduce the information to be processed under the premise of retaining the shape information of the object, the extraction accuracy of the edge feature affects the accuracy of the subsequent processing of the extracted feature, so edge detection is an important aspect of image processing and computer vision basic questions in . The edge of the grayscale image refers to the most significant part of the grayscale change in the local area of ​​the image, and the grayscale change of the image can be expressed by gradient. Currently, commonly used edge extraction methods are mainly divided into two categories: first-order differential edge detection algorithms (such as Sobel, Prewitt algorithm)...

AI Technical Summary

Problems solved by technology

[0003] However, the traditional Canny edge detection algorithm has problems such as poor denoising effect on noisy images, and the need to pre-set the double threshold.

Images