gusucode.com > 利用遗传算法进行图像分割matlab源码程序 > segment_ga/2d_ksw_qiongju.m
%%%利用二维最佳直方图熵法(KSW熵法)及穷举法实现灰度图像阈值分割 %%%主程序 %%初始部分,读取图像及计算相关信息 clear; close all; clc; %format long; I=imread('rice.tif'); windowsize=3; I_temp=I; for i=2:255 for j=2:255 I_temp(i,j)=round(mean2(I(i-1:i+1,j-1:j+1))); end end I_average=I_temp; I_p=I; I_average_p=I_average; hist_2d(1:256,1:256)=zeros(256,256); for i=1:256 for j=1:256 hist_2d(I_p(i,j),I_average_p(i,j))=hist_2d(I_p(i,j),I_average_p(i,j))+1; end end total=256*256; hist_2d_1=hist_2d/total; %%%%%% Hst=0; for i=0:255 for j=0:255 if hist_2d_1(i+1,j+1)==0 temp=0; else temp=hist_2d_1(i+1,j+1)*log(1/hist_2d_1(i+1,j+1)); end Hst=Hst+temp; end end %%程序主干部分 t0=clock; for s=0:255 for t=0:255 adapt_value(s+1,t+1)=ksw_2d(s,t,0,255,hist_2d_1,Hst); end end [max_value1,index1]=max(adapt_value); [max_value2,index2]=max(max_value1); t_opt=index2-1; s_opt=index1(index2)-1; t1=clock; search_time=etime(t1,t0); %%阈值分割及显示部分 threshold_opt=s_opt/255; I1=im2bw(I,threshold_opt); disp('灰度图像阈值分割的效果如图所示:'); disp('源图为:Fifure No.1'); disp('二维最佳直方图熵法及穷举法阈值分割后的图像为:Fifure No.2'); figure(1); imshow(I); title('源图'); figure(2); imshow(I1); title('二维最佳直方图熵法及穷举法阈值分割后的图像'); disp('二维最佳直方图熵法及穷举法阈值为(s,t):'); disp(s_opt); disp(t_opt); disp('二维最佳直方图熵法及穷举法阈值搜索所用时间(s):'); disp(search_time); %%程序结束