Image threshold segmentation method based on Renyi cross entropy and Gaussian distribution

Abstract

Provided is an image gray histogram threshold segmentation method based on Renyi cross entropy and Gaussian distribution. The method comprises the following steps: 1) initializing Renyi cross entropy index alpha; 2) reading a grayscale image to be segmented and storing the image to a two-dimensional image array I; 3) traversing the image array I and calculating the maximum image gray level L and a gray level set G={0,1,...,L}; 4) supposing t is segmentation threshold value, and based on the t, dividing image pixels into two different kinds of gray level sets C0 and C1; 5) calculating prior probability P0 and P1, gray level mean values M0 and M1 and class variance S0 and S1 with respect to the C0 and C1 through formulas, and class probability P0 and P1 of each gray level i of the image with respect to the C0 and C1, and normalized posterior probability of each image gray level i obtained through Gaussian fitting; defining a symmetrical information amount formula of the image with respect to Renyi cross entropy; obtaining optimal segmentation threshold value; and finally, outputting an image obtained after segmentation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image threshold segmentation method based on Renyi cross-entropy and Gaussian distribution in the technical field of image segmentation. Background technique [0002] Image segmentation is a key link in image processing tasks based on machine vision. It is the basis for image target feature extraction, recognition, detection, and image analysis and understanding. Among many image segmentation methods, the threshold segmentation method has become a widely used segmentation technique in scientific research and application practice due to its simplicity, effectiveness and ease of implementation. [0003] Threshold segmentation techniques can be divided into parametric and non-parametric methods. Since the non-parametric method only needs to design a criterion function when performing image segmentation, and there are few estimations of a large number of parameters...

AI Technical Summary

Problems solved by technology

[0004] An image is a complex physical system, so far there is no universal segmentation method suitable for all image segmentation tasks, so in the face of different segmentation tasks (such as the segmentation of NDT images), research and propose effective Segmentation methods are still a challenging task at present

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