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Mode search algorithm and firefly algorithm-based image multi-threshold segmentation method
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A pattern search algorithm and firefly algorithm technology, applied in the field of image processing, can solve the problems of slow segmentation, increased traversal evaluation solution space, and inability to meet application real-time requirements.
Inactive Publication Date: 2017-12-26
SICHUAN CHANGHONG ELECTRIC CO LTD
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[0006] When the traditional image threshold segmentation method based on the maximum inter-class variance method is extended from single-threshold segmentation to multi-threshold segmentation, as the number of thresholds increases, the solution space that requires traversal evaluation increases, and the segmentation achieved by the traditional exhaustive method The speed becomes slower and cannot meet the real-time requirements of the application
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Embodiment 1
[0038] Such as figure 1 As shown, an image multi-threshold segmentation method based on pattern search algorithm and firefly algorithm, the method steps are as follows:
[0039] A. Obtain the grayscale image I that needs to be segmented by multiple thresholds, and determine the number d of thresholds;
[0040] B. Set the algorithm parameters of firefly algorithm and pattern search algorithm, namely N=20, MaxT=200, γ=1, β 0 =0.8, Algorithm parameters parameters=[ρ,α,τ,ρ min ]=[1,1,0.5,1], initial step size ρ=1, acceleration factor α=1, reduction rate τ=0.5, minimum step size ρ min = 1;
[0041] C. Initialize the population X according to the parameters of the firefly algorithm i ;
[0042] D. According to Calculate the fitness value corresponding to the population;
[0043] E. Select the optimal segmentation threshold X g ;
[0044] F. Judging whether the algorithm iteration reaches the maximum number of iterations MaxT, if reached, then jump to step K, otherwise, jum...
Embodiment 2
[0061] Such as figure 1 As shown, an image multi-threshold segmentation method based on pattern search algorithm and firefly algorithm, the method steps are as follows:
[0062] Step1: Obtain the grayscale image I that needs to be segmented by multiple thresholds, and determine the number of thresholds d;
[0063] Step2: Set the algorithm parameters of firefly algorithm and pattern search algorithm, namely N=20, MaxT=200, γ=1, β 0 =0.8, parameters=[ρ,α,τ,ρ min ]=[1,1,0.5,1];
[0064] Step3: Initialize the population X according to the parameters of the firefly algorithm i
[0065] step4: According to Calculate the fitness value corresponding to the population;
[0066] step5: Select the optimal solution (that is, the optimal segmentation threshold) X g ;
[0067] Step6: Determine whether the algorithm iteration reaches the maximum number of iterations MaxT, if so, then jump to step12, otherwise, jump to step7;
[0068] step7: with X g As the base point, with param...
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Abstract
The invention discloses a mode search algorithm and firefly algorithm-based image multi-threshold segmentation method. The method comprises the steps of firstly obtaining a grayscale image needed to be processed, and if the grayscale image is a color image, converting the color image into a grayscale image I(x,y) through rgb2gray() of MATLAB 7.12; and then performing threshold segmentation through an OTSU method based on a mode search algorithm and a firefly algorithm: specifically, the OTSU method based on the mode search algorithm and the firefly algorithm is that thresholds=PS-FA-OTSU(grayImage,num), wherein grayImage is a to-be-segmented grayscale image input in the algorithms, num is a to-be-segmented threshold number, thresholds are segmented thresholds obtained by algorithm-based segmentation, and the output thresholds are the obtained segmented thresholds, so that the threshold segmentation of the grayscale image is finished. The OTSU method-based grayscale image multi-threshold segmentation is quicker, more stable and more efficient.
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