城市地區(qū)道路網的簡單的閾值分割。采用的是單ostu（最佳閾值分割）算法，廢話少說，如果不太清楚該算法，請參考文獻[1]中的圖像分割這一章的介紹。程序直接運行的效果如下。

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直接附加代碼，希望對大家有一些益處，節(jié)約你的時間：

% 最大類方差法實現自適應圖像閾值分割 % This implements is implemented by WENG, All rights reserved % Copy Rights (c) WENG % 2016-3-30 1639clear;clc;close all; warning off; %#ok<WNOFF> IMG = imread('steger_perfect_1_rg.tif');% fdoa1_rs1_rg.tif IMG_gray=double(rgb2gray(IMG)); [M,N]=size(IMG_gray);gray_level=256;% Histgram Histgram_normal=zeros(256,1);% value span, 1~256 for i_level=1:256,Histgram_normal(i_level)=sum(sum(IMG_gray==(i_level+1))); end Histgram_normal=Histgram_normal/(M*N); % histgram normalizem_overall=0; for m=1:gray_level,m_overall=m_overall+(m-1)*Histgram_normal(m);endsigma_max=0; sigma_B=zeros(gray_level,1); for i=1:gray_level,threshold=i-1;% calculate the number of the two class, the number is 0~1num1=0; for m=1:threshold-1num1=num1+Histgram_normal(m);endnum2=1-num1;% cal m1, m2m1=0;m2=0;for m=1:gray_levelif m<thresholdm1=m1+(m-1)*Histgram_normal(m);elsem2=m2+(m-1)*Histgram_normal(m);endend% calculate the std betweent the two classesm1=m1/num1;m2=m2/num2;sigma1=num1*(m1-m_overall)^2+num2*(m2-m_overall)^2;% save and updatesigma_B(i)=sigma1;if sigma1>sigma_maxoptical_T=threshold;sigma_max=sigma1;end endTh_optical=optical_T; Th_matalb=graythresh(IMG_gray)*255;%matlab函數求閾值 fprintf(1,'The otsu threshold and Threshold calculate by graythresh are:\n %.1f [best], %.1f\n\n',Th_optical,Th_matalb);% visual the result Threshold_Optimal_IMG=zeros(M,N); Threshold_graythresh_IMG=zeros(M,N); Threshold_Optimal_IMG(IMG_gray>Th_optical)=1; Threshold_graythresh_IMG(IMG_gray>Th_matalb)=1; figure,subplot(2,2,1);imshow(IMG,[]);title('Ori color image'); subplot(2,2,2);imshow(IMG_gray,[]);title('Ori gray image'); subplot(2,2,3);imshow(Threshold_graythresh_IMG,[]);title('Threshold calculated by graythresh'); subplot(2,2,4);imshow(Threshold_Optimal_IMG,[]);title('Threshold calculated by ostu');figure,subplot(1,2,1); hold on; plot(1:gray_level,sigma_B);title('The two TH marked on \sigma array'); xlabel('Th'); ylabel('\sigma_B'); plot(Th_optical+1,sigma_B(Th_optical+1),'xg','MarkerSize',8,'LineWidth',2); text(Th_optical+1,sigma_B(Th_optical+1)-50,'otsu threshold'); plot(Th_matalb+1,sigma_B(Th_matalb+1),'^r','MarkerSize',8,'LineWidth',2); text(Th_matalb+1,sigma_B(Th_matalb+1)-50,'graythresh calculated threshold'); hold off;subplot(1,2,2);plot(1:gray_level,Histgram_normal);title('Gray image Histgram'); hold on; plot(Th_optical+1,Histgram_normal(Th_optical+1),'xg','MarkerSize',8,'LineWidth',2); text(Th_optical+1,Histgram_normal(Th_optical+1)-50,'otsu threshold'); plot(Th_matalb+1,Histgram_normal(Th_matalb+1),'^r','MarkerSize',8,'LineWidth',2); text(Th_matalb+1,Histgram_normal(Th_matalb+1)-50,'graythresh calculated threshold'); hold off;% Image that can be classified by the threshold evaluate Sigma_G=std(IMG_gray(:)); eta_ostu=sigma_B(Th_optical+1)/Sigma_G; eta_matlab=sigma_B(Th_matalb+1)/Sigma_G; fprintf(1,'The separability of ostu and matlab graythresh func respectively are:\n%.3f, %.3f\n',eta_ostu,eta_matlab);

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參考文獻

[1] (美)岡薩雷斯(Gonzalez, R.C.), (美)伍茲(Woods,等. 數字圖像處理[M]. 電子工業(yè)出版社, 2013.

沒有整理與歸納的知識，一文不值！高度概括與梳理的知識，才是自己真正的知識與技能。 永遠不要讓自己的自由、好奇、充滿創(chuàng)造力的想法被現實的框架所束縛，讓創(chuàng)造力自由成長吧！ 多花時間，關心他（她）人，正如別人所關心你的。理想的騰飛與實現，沒有別人的支持與幫助，是萬萬不能的。



本文轉自wenglabs博客園博客，原文鏈接：http://www.cnblogs.com/arxive/p/5338000.html，如需轉載請自行聯(lián)系原作者

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