Gray scale image threshold segmentation method based on Alpha-Gamma divergence

Abstract

The invention discloses a gray scale image threshold segmentation method based on Alpha-Gamma divergence. The gray scale image threshold segmentation method comprises the steps of inputting a to-be-segmented image and calculating a normalized gray scale histogram, constructing an Alpha-Gamma divergence expression of the image before and after segmentation, calculating a gray scale limit value which leads to smallest value of the expression in the image gray scale range, performing threshold segmentation on the image by means of the gray scale limit value, outputting the segmented images, etc. The gray scale image threshold segmentation method has advantages of effective improvement of image segmentation quality, accurate edge contour of the segmented image, and clear texture details. Furthermore universality of the method is improved. The gray scale image threshold segmentation method is suitable for image processing tasks with high real-time performance requirements.

Description

technical field [0001] The invention relates to the field of image segmentation in machine vision, specifically a threshold segmentation method based on Alpha-Gamma divergence of grayscale image histogram information to realize rapid and accurate segmentation of industrial images such as non-destructive testing based on machine vision. Background technique [0002] Image segmentation is one of the most basic but also the most difficult and challenging problems in image processing. The purpose of image segmentation is to divide the image into multiple regions that do not overlap with each other, and the internal objects in each region are homogeneous, so as to lay the foundation for the subsequent processing of the image. Due to the influence of many factors in the image imaging process, its complexity also makes the segmentation method not universally applicable to different segmentation tasks. Therefore, researching new methods for specific segmentation tasks in practice ha...

AI Technical Summary

Problems solved by technology

The minimum cross-entropy thresholding method proposed by Li and Lee is the most famous image threshold segmentation method based on the concept of cross-entropy (that is, information divergence, relative entropy). In addition to this method, other famous thresholding methods related to the concept of cross-entropy There is also the minimum error thresholding method proposed by Kittler and Illingworth. This method is essentially a relative entropy method based on the concept of mean square error of Euclidean distance. The mean square error cannot completely and effectively distinguish the relationship between image pixels. Therefore, there are also deficiencies in the segmentation of images

In addition, Chinese scholar Tang Yingqian and others proposed a minimum Tsallis cross-entropy method based on the uniform distribution based on the Tsallis cross-entropy. However, in the real environment, the pixel distribution of the image does not always obey the uniform distribution, so the segmentation performance of the method It also needs to be improved; based on the chi-square divergence (χ 2 -divergence) method is another image thresholding method based on the concept of divergence proposed by related scholars. This method is very sensitive to the distribution of the histogram, and a good threshold cannot be obtained when the histogram is unevenly distributed.

Images