Method for detecting edge of color image on basis of local self-adaption color difference threshold

Abstract

The invention discloses a method for detecting an edge of a color image on the basis of a local self-adaption color difference threshold and belongs to the field of image processing and computer vision. The method comprises the following steps: firstly, establishing a weighting function containing a color difference threshold of a background luminance mask and contrast ratio sensitivity function; confirming the color difference threshold of each pixel point according to neighborhood information; if a calculating result of a color difference gradient operator of the pixel point is greater than the threshold, judging the present pixel as an edge point and displaying; and if not, setting the pixel luminance as zero. A test proves that according to the method provided by the invention, the luminance mask effect of human eyes and contrast ratio sensitivity characteristics are considered, the visual sensing characteristics of the human eyes are approached, an image edge sensed by the human eyes is effectively detected, and meanwhile, the problem of edge over-detection caused by threshold judgment in the traditional algorithm is avoided, and the noise resistance is better.

Description

technical field [0001] The invention relates to the fields of image processing and computer vision, in particular to a color image edge detection method that simulates human eyes' perception of image information and has good robustness to noise. Background technique [0002] The edge of the image is defined as a discontinuous point in the image function, which contains most of the features of the image, and is the key to distinguish between objects and backgrounds, regions of interest and surrounding information. The existing edge detection mainly converts the image to be processed into a grayscale image, and regards the edge of the image as a collection of points with sudden changes in the gray value in the neighborhood. The classic operators include Sobel operator, SUSAN operator, Laplace operator, Canny operator etc. Due to the lack of color information, these algorithms cannot distinguish objects with the same brightness but different colors, and are prone to missed det...

AI Technical Summary

Problems solved by technology

For this type of algorithm, the selection of the threshold is very important. The existing algorithm sets a single color difference threshold as the global threshold. This threshold selection method ignores the influence of the local information of the image on the perception of human color difference, making many invisible The edges of are over-detected and less robust to noise

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