【图像分割】基于粒子群、文化、进化策略算法实现化石图像分割附matlab代码

1 内容介绍

Start

Loading Image

Training Using Evolution Strategy Algorithm (Input: Raw Image and Target Histogram Vector)

Goal: To Adjusting the Intensity by Equalizing the Image Histogram

Initialize the Population Size N and Number of Generations

While (number of generations is not reached)

Recombination of Attributes and Variances of Individuals

Mutation of Attributes and Variances

Evaluation of Fitness Function for Individuals

Selection for New and Best Individuals (Best Target Histogram Value)

End While

Apply Best Selected Target Histogram Vector

End of ES

Output: Evolutionary Intensity Adjusted of Target Histogram for Raw Input Image

Training Using Cultural Algorithm (Input: ES Equalized Histogram Image and Threshold Levels Vector)

Goal: To Quantize the Image by Fitting Threshold Level

Initialize the Population Size N and Number of Generations

While (number of generations is not reached)

Fitness Evaluation

Updating Belief Space

Reproduction Operators

Influence and Acceptance Functions

Selecting Best Individuals (Best Threshold Value)

End While

Apply Best Selected Threshold Level Vector

End of CA

Output: Evolutionary Fitted Threshold Level for Input Image

Training Using Simulated Annealing Algorithm (Input: Quantized Image and Filter Matrix)

Goal: To Select Best Edge Filters

Initialize the Population Size N and Number of Generations

While (number of generations is not reached)

Objective Function Evaluation

If Objective Function Decreases

Update the Best Solution for Each Filter Vector

Reduce the Current Temperature

Generate a New Trial Solution and Go to Evaluation Step

Else If Metropolis Criterion Is Meet

Go to Update Step

Else

Go to Reduce Temperature Step

End While

Apply Best Selected Edge Filter

End of SA

Output: Evolutionary Edge Detected Image

Training Using Particle Swarm Optimization + SA (Input: Edge Detected Input Image)

Goal: To Segment the Input Image

Initialize the Population Size N and Number of Generations

While (number of generations is not reached)

Initialized Particles with Random Position and Velocity for PSO

Evaluate the Fitness of Particles for each Pixel and Their Corresponding Distance for PSO

Objective Function Evaluation for SA as Optimizer

Find and update pbest and gbest for PSO

Reduce The Current Temperature for SA

Calculate and Update Velocity and Position for PSO

Generate a New Trial Solution and Go to Evaluation Step for SA

Show gbest the Optimal Solution for PSO

Desirable Temperature Reached for SA

Update the Best Solution Found for Pixel and Distance by PSO+SA

End While

Apply Best Clusters Found on Image to Segment

End of PSOSA

Overlay All Evolutionary Techniques

Output: Evolutionary Segmented Image

End​

2 仿真代码

function Culture = AdjustCulture(Culture, spop)

n = numel(spop);

nVar = numel(spop(1).Position);

for i = 1:n

if spop(i).Cost

Culture.Situational = spop(i);

end

for j = 1:nVar

if spop(i).Position(j)

|| spop(i).Cost

Culture.Normative.Min(j) = spop(i).Position(j);

Culture.Normative.L(j) = spop(i).Cost;

end

if spop(i).Position(j)>Culture.Normative.Max(j) ...

|| spop(i).Cost

Culture.Normative.Max(j) = spop(i).Position(j);

Culture.Normative.U(j) = spop(i).Cost;

end

end

end

Culture.Normative.Size = Culture.Normative.Max-Culture.Normative.Min; 

end

3 运行结果

4 参考文献

[1]余胜威. 基于FODPSO算法的图像分割及DSP实现[D]. 西南交通大学, 2016.

[2]王建宾. 基于粒子群优化絮体图像分割算法的设计和应用[D]. 华东交通大学.

Mousavi, S. M. H. (2022). Bio-Inspired Fossil Image Segmentation for %% Paleontology. International Journal of Mechatronics, Electrical and %% Computer Technology (IJMEC), 12(45), 5243-5249.

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