matlab怎么实现图像的自适应多阈值快速分割
今天小编给大家分享一下matlab怎么实现图像的自适应多阈值快速分割的相关知识点，内容详细，逻辑清晰，相信大部分人都还太了解这方面的知识，所以分享这篇文章给大家参考一下，希望大家阅读完这篇文章后有所收获，下面我们一起来了解一下吧。
1 内容介绍
为快速准确地将图像中目标和背景分离开来,将新型群体智能模型中的花朵授粉算法、最大类间阈值相结合,提出了一种图像分割新方法.该方法将图像阈值看成花朵授粉算法群算法中的花粉，利用信息熵和最大熵原理设计花朵授粉算法的适应度函数,逐代逼近最佳阈值.并利用Matlab实现了图像分割算法,对分割的结果进行分析.实验结果表明,该方法在阈值分割图像时,花朵授粉算法能够快速准确地将图像目标分离出来,分离出来的目标更加适合后序的分析和处理.
2 部分代码
% --------------------------------------------------------------------%
% Flower pollenation algorithm (FPA), or flower algorithm %
% Programmed by Xin-She Yang @ May 2012 %
% --------------------------------------------------------------------%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Notes: This demo program contains the very basic components of %
% the flower pollination algorithm (FPA), or flower algorithm (FA), %
% for single objective optimization. It usually works well for %
% unconstrained functions only. For functions/problems with %
% limits/bounds and constraints, constraint-handling techniques %
% should be implemented to deal with constrained problems properly. %
% %
% Citation details: %
%1)Xin-She Yang, Flower pollination algorithm for global optimization,%
% Unconventional Computation and Natural Computation, %
% Lecture Notes in Computer Science, Vol. 7445, pp. 240-249 (2012). %
%2)X. S. Yang, M. Karamanoglu, X. S. He, Multi-objective flower %
% algorithm for optimization, Procedia in Computer Science, %
% vol. 18, pp. 861-868 (2013). %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc
clear all
close all
n=30; % Population size, typically 10 to 25
p=0.8; % probabibility switch
% Iteration parameters
N_iter=3000; % Total number of iterations
fitnessMSE = ones(1,N_iter);
% % Dimension of the search variables Example 1
d=2;
Lb = -1*ones(1,d);
Ub = 1*ones(1,d);
% % Dimension of the search variables Example 2
% d=3;
% Lb = [-2 -1 -1];
% Ub = [2 1 1];
%
% % Dimension of the search variables Example 3
% d=3;
% Lb = [-1 -1 -1];
% Ub = [1 1 1];
%
%
% % % Dimension of the search variables Example 4
% d=9;
% Lb = -1.5*ones(1,d);
% Ub = 1.5*ones(1,d);
% Initialize the population/solutions
for i=1:n,
Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);
% To simulate the filters use fitnessX() functions in the next line
Fitness(i)=fitness(Sol(i,:));
end
% Find the current best
[fmin,I]=min(Fitness);
best=Sol(I,:);
S=Sol;
% Start the iterations -- Flower Algorithm
for t=1:N_iter,
% Loop over all bats/solutions
for i=1:n,
% Pollens are carried by insects and thus can move in
% large scale, large distance.
% This L should replace by Levy flights
% Formula: x_i^{t+1}=x_i^t+ L (x_i^t-gbest)
if rand>p,
%% L=rand;
L=Levy(d);
dS=L.*(Sol(i,:)-best);
S(i,:)=Sol(i,:)+dS;
% Check if the simple limits/bounds are OK
S(i,:)=simplebounds(S(i,:),Lb,Ub);
% If not, then local pollenation of neighbor flowers
else
epsilon=rand;
% Find random flowers in the neighbourhood
JK=randperm(n);
% As they are random, the first two entries also random
% If the flower are the same or similar species, then
% they can be pollenated, otherwise, no action.
% Formula: x_i^{t+1}+epsilon*(x_j^t-x_k^t)
S(i,:)=S(i,:)+epsilon*(Sol(JK(1),:)-Sol(JK(2),:));
% Check if the simple limits/bounds are OK
S(i,:)=simplebounds(S(i,:),Lb,Ub);
end
% Evaluate new solutions
% To simulate the filters use fitnessX() functions in the next
% line
Fnew=fitness(S(i,:));
% If fitness improves (better solutions found), update then
if (Fnew<=Fitness(i)),
Sol(i,:)=S(i,:);
Fitness(i)=Fnew;
end
% Update the current global best
if Fnew<=fmin,
best=S(i,:) ;
fmin=Fnew ;
end
end
% Display results every 100 iterations
if round(t/100)==t/100,
best
fmin
end
fitnessMSE(t) = fmin;
end
%figure, plot(1:N_iter,fitnessMSE);
% Output/display
disp(['Total number of eval（N_iter*n)]);
disp(['Best solution=',num2str(best),' fmin=',num2str(fmin)]);
figure(1)
plot( fitnessMSE)
xlabel('Iteration');
ylabel('Best score obtained so far');
3 运行结果
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