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Grayscale-gradient entropy multi-threshold fast division method based on genetic algorithm

A genetic algorithm and gradient entropy technology, applied in the field of digital image processing, can solve the problems of multi-objective and complex images that cannot be effectively segmented.

Inactive Publication Date: 2015-05-20
KUNMING UNIV OF SCI & TECH
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  • Abstract
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Problems solved by technology

Although the grayscale-gradient (GLGM) entropy algorithm can effectively solve the above problems, it cannot effectively segment multi-target and complex images.

Method used

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  • Grayscale-gradient entropy multi-threshold fast division method based on genetic algorithm
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  • Grayscale-gradient entropy multi-threshold fast division method based on genetic algorithm

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Embodiment 1

[0055] Embodiment 1: as Figure 1-16 As shown, a grayscale-gradient entropy multi-threshold fast segmentation method based on genetic algorithm, first input an image to be segmented in Matlab, and obtain the grayscale-gradient histogram of the image; then use the grayscale-gradient histogram Calculate the information entropy of the image to obtain the grayscale-gradient entropy function, and then use the genetic algorithm based on real number coding to calculate the solution of the function when the grayscale-gradient entropy function reaches the maximum value, and finally according to the obtained solution, The pixels of the image are redistributed, and the image is reconstructed to obtain the segmentation result.

[0056] The specific steps for obtaining the grayscale-gradient histogram of the image are as follows:

[0057] Step1.1. Input an image I(x,y) to be segmented in Matlab for sobel processing, and obtain the gradient magnitude image I(x 1 ,y 1 ), the corresponding...

Embodiment 2

[0085] Embodiment 2: as Figure 1-16 As shown, a grayscale-gradient entropy multi-threshold fast segmentation method based on genetic algorithm, first input an image to be segmented in Matlab, and obtain the grayscale-gradient histogram of the image; then use the grayscale-gradient histogram Calculate the information entropy of the image to obtain the grayscale-gradient entropy function, and then use the genetic algorithm based on real number coding to calculate the solution of the function when the grayscale-gradient entropy function reaches the maximum value, and finally according to the obtained solution, The pixels of the image are redistributed, and the image is reconstructed to obtain the segmentation result.

[0086] The specific steps for obtaining the grayscale-gradient histogram of the image are as follows:

[0087] Step1.1. Input an image I(x,y) to be segmented in Matlab for sobel processing, and obtain the gradient magnitude image I(x 1 ,y 1 ), the corresponding...

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Abstract

The invention relates to a grayscale-gradient entropy multi-threshold fast division method based on a genetic algorithm, and belongs to the technical field of digital image processing. According to the method, firstly, an image to be divided is input in a MATLAB (matrix laboratory), a grayscale-gradient histogram of the image is obtained; then, the grayscale-gradient histogram is used for calculating an information entropy of the image to obtain a grayscale-gradient entropy function, then, the genetic algorithm based on real number encoding is used for calculating the solution of the function when the grayscale-gradient entropy function obtains the maximum value, finally, pixels of the image are distributed again according to the obtained solution, the image is rebuilt, and the division result is obtained. The grayscale-gradient entropy multi-threshold fast division method has the advantages that the single-threshold image is divided and expanded to multi-threshold division, the multi-target image can be effectively divided, and in addition, the operation time is shorter.

Description

technical field [0001] The invention relates to a gray scale-gradient entropy multi-threshold rapid segmentation method based on a genetic algorithm, which belongs to the technical field of digital image processing. Background technique [0002] In recent years, many threshold segmentation techniques have been proposed, among which the entropy-based threshold technique has increasingly become a focus of attention. Pun first introduced the concept of entropy in image segmentation for entropy threshold segmentation. Kapur et al. proposed an improved algorithm for the deficiency of Pun theory. In order to solve the problem that the information of gray space distribution is not considered in Kapur theory, Abutaleb proposed a threshold method based on two-dimensional histogram entropy, which distinguishes pixels by calculating the gray value by using the average pixel value of the corresponding area. In addition, Xiao distinguishes the characteristics of pixels by observing whe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/12
CPCG06N3/12G06T7/12
Inventor 贺建峰符增
Owner KUNMING UNIV OF SCI & TECH
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