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An image segmentation method based on directional crossover genetic algorithm and two-dimensional maximum entropy threshold segmentation algorithm

A two-dimensional maximum entropy and cross genetic technology, applied in the field of image segmentation based on directed cross genetic algorithm and maximum entropy method, can solve problems such as blindness, achieve the effect of improving accuracy, reducing the number of calculations, and improving efficiency

Active Publication Date: 2018-12-18
BEIJING UNIV OF TECH
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Problems solved by technology

Through the introduction of the directed crossover operator, the problem of blindness in the offspring generation of the crossover operator of the genetic algorithm is effectively solved, the number of entropy function calculations is reduced, and the accuracy and efficiency of solving the segmentation threshold are greatly improved.

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  • An image segmentation method based on directional crossover genetic algorithm and two-dimensional maximum entropy threshold segmentation algorithm
  • An image segmentation method based on directional crossover genetic algorithm and two-dimensional maximum entropy threshold segmentation algorithm
  • An image segmentation method based on directional crossover genetic algorithm and two-dimensional maximum entropy threshold segmentation algorithm

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Embodiment Construction

[0031] The present invention will be described below in conjunction with the accompanying drawings and specific embodiments. The method proposed by the present invention is applied to image segmentation, and the overall flow chart is as follows figure 1 , the specific implementation steps are as follows:

[0032] (1) Obtain image information, calculate the image gray value and average gray value, and count the two-dimensional histogram;

[0033] (2) According to the threshold value range of the histogram, N gray value-average gray value data pairs are randomly generated, and these N data pairs are directly used as the initial population individuals of the directed crossover genetic algorithm, and N is taken in this embodiment =20;

[0034] (3) Calculate the two-dimensional entropy value of the population individual as the fitness value, and select the individual with the largest fitness value as the initial elite individual. For the gray value-average gray value data pair (x...

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Abstract

The invention discloses an image segmentation method based on a directional crossover genetic algorithm and a two-dimensional maximum entropy threshold segmentation algorithm. The method comprises thesteps of: obtaining an image and calculating a two-dimensional histogram; generating an initial population; calculating fitness values and preserving elite individuals; setting algorithm parameters;performing genetic manipulation on the population which includes selection, directed crossover, mutation, and calculating the fitness value of mutant individuals; retaining elite individuals; judgingwhether the termination condition is satisfied; if the condition is satisfied, obtaining the optimal individual as a threshold for image segmentation;. if the condition is not satisfied, returning tothe genetic operation step. The invention extends the directional crossing genetic algorithm to the two-dimensional case and combines the algorithm with the two-dimensional maximum entropy image segmentation, solves the problem that the genetic algorithm has blindness when the image threshold value is optimized, and greatly improves the speed and accuracy of image segmentation using the genetic algorithm.

Description

technical field [0001] The invention relates to the technical field of digital image processing and genetic algorithm, in particular to an image segmentation method based on directed crossover genetic algorithm and maximum entropy method. Background technique [0002] As a class of classic image problems, image segmentation is the basis of high-level work in digital image processing. It has attracted extensive attention from researchers, and a large number of segmentation methods have been proposed. Common image segmentation methods include region growing method, watershed method, cluster segmentation method, edge detection method, threshold segmentation method and so on. Most threshold segmentation methods only need to solve the maximum value of the specified criterion function as the segmentation threshold to achieve image segmentation. Compared with other segmentation algorithms, they are simpler and more efficient, and have been widely studied and applied in the field of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/136G06N3/12
CPCG06N3/126G06T2207/10004G06T2207/20081G06T7/11G06T7/136
Inventor 范青武李岚泊陈光晃周星奇
Owner BEIJING UNIV OF TECH
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