A multi-threshold image segmentation method based on adaptive cuckoo optimization

A cuckoo and multi-threshold technology, applied in the field of image processing, can solve the problems of long time consumption and low precision, and achieve the effects of expanding the search range, improving real-time performance, and strong local development capabilities

Active Publication Date: 2019-01-18
ANHUI UNIV OF SCI & TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an image multi-threshold segmentation method based on the adaptive cuckoo optimization method to solve the problems of long time consumption and low precision of the traditional maximum entropy method

Method used

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  • A multi-threshold image segmentation method based on adaptive cuckoo optimization
  • A multi-threshold image segmentation method based on adaptive cuckoo optimization
  • A multi-threshold image segmentation method based on adaptive cuckoo optimization

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Embodiment

[0051] Such asfigure 1 As shown, the threshold selection criterion of the multi-threshold maximum entropy method in this embodiment is that the total entropy value of the segmented object class and the background class is the largest. Its specific implementation steps are as follows:

[0052] (1) Image preprocessing

[0053] figure 1 A flowchart of this embodiment is given. Read in the grayscale image that needs to be processed, that is, the image to be segmented, and determine the number of thresholds.

[0054] (2) Setting the objective function

[0055] The maximum entropy method is selected as the objective function, and the maximum entropy method is determined by the following formula:

[0056]

[0057] Among them, H i (t 1 ,t 2 ,...,t k ) is the fitness function value of the i-th individual, i is a finite positive integer, t 1 ,t 2 ,...,t k is the segmentation threshold, pi is the probability of the i-th grayscale appearing, k is the number of thresholds, ...

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Abstract

The invention relates to an image multi-threshold segmentation method based on an adaptive cuckoo optimization method, comprising the steps of obtaining a gray-scale image to be processed, setting anobjective function, searching an optimal threshold value by the adaptive cuckoo optimization method, and performing image multi-threshold segmentation. The initial position of the bird 's nest is located within the pixel size boundary value range of the gray scale image, The maximum entropy is used as the fitness function of the method, and the fitness value is used to evaluate the position of each bird's nest. The bird's nest position is updated through the iterative process of continuous Levy flight and random preference walk, and the global optimal threshold is found quickly and accurately,and then the image is segmented. Compared with the prior art, the invention has the advantages of high segmentation threshold accuracy, better real-time performance, etc., and can be used for color image and gray image segmentation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image multi-threshold segmentation method based on an adaptive cuckoo optimization method. Background technique [0002] Image segmentation is essentially a classification problem, the purpose is to divide an image into several regions with a certain uniformity, so as to extract one or more objects in the image. Threshold segmentation method is a traditional image segmentation method, which has the characteristics of clear physical meaning and easy implementation. When extended to image multi-threshold segmentation, the search space is large, the calculation complexity is high and the calculation time is long, so the traditional exhaustive method cannot achieve good real-time performance. [0003] The maximum entropy threshold method is to maximize the total entropy of the segmented image target class and background class, that is, use several thresholds ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/136G06T7/11G06T7/194G06N3/00
CPCG06N3/006G06T7/11G06T7/136G06T7/194G06T2207/20004
Inventor 孙敏韦慧
Owner ANHUI UNIV OF SCI & TECH
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