《MATLAB图像处理实例详解》：CH_7（图像分割技术）

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## 图像分割技术 ##

①提取线段

close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\xianduan.jpg');
    I=rgb2gray(I);
    h1=[-1 -1 -1;2 2 2;-1 -1 -1];%横线
    h2=[-1 -1 2;-1 2 -1;2 -1 -1];%45°斜线
    h3=[-1 2 -1;-1 2 -1;-1 2 -1];%竖线
    h4=[2 -1 -1;-1 2 -1;-1 -1 2];%135°斜线
    J1=imfilter(I,h1);
    J2=imfilter(I,h2);
    J3=imfilter(I,h3);
    J4=imfilter(I,h4);
    J=J1+J2+J3+J4;
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);

②微分算子

%Roberts算子
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\gray.jpg');
    I=im2double(I);
    [J,thresh]=edge(I,'roberts',35/255);%边缘检测，采用Roberts算子，图像分割阈值为35/255
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);
    
    %prewitt算子
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\gray.jpg');
    I=im2double(I);
    [J,thresh]=edge(I,'prewitt',[],'both');%边缘检测，水平和垂直边缘都检测
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);
    
    %sobel算子
    %eg.1
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\gray.jpg');
    I=im2double(I);
    [J,thresh]=edge(I,'sobel',[],'horizontal');%边缘检测，检测水平边缘
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);
    
    %eg.2
    close all;clear all;clc;
    I = imread('E:\Matlab_exercise\图片素材\lenn.jpg');
    subplot(241);imshow(I,[]);title('原图');
    
    J1 = edge(I,'sobel',0.06);
    subplot(242);imshow(J1,[]);title('sobel 0.06');
    J2 = edge(I,'sobel',0.09);
    subplot(243);imshow(J2,[]);title('sobel 0.09');
    J3 = edge(I,'sobel',0.12);
    subplot(244);imshow(J3,[]);title('sobel 0.12');
    %滤波
    sigma = 0.6;%滤波器的标准差
    N = 5;%滤波模板大小是（2N+1）*（2N+1）,默认为3*3的矩阵
    N_row = 2*N+1;
    gausFilter = fspecial('gaussian',[N_row N_row],sigma);%高斯滤波模板
    K = imfilter(I,gausFilter,'conv');
    subplot(245);imshow(K,[]);title('原图滤波后');
    
    K1 = edge(K,'sobel',0.06);
    subplot(246);imshow(K1,[]);title('sobel 0.06');
    K2 = edge(K,'sobel',0.09);
    subplot(247);imshow(K2,[]);title('sobel 0.09');
    K3 = edge(K,'sobel',0.12);
    subplot(248);imshow(K3,[]);title('sobel 0.12');
    
    %canny算子
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\grayBeauty.jpg');
    I=im2double(I);
    J=imnoise(I,'gaussian',0,0.01);
    [K,thresh]=edge(J,'canny');
    figure;
    subplot(121);imshow(J);
    subplot(122);imshow(K);
    
    %LOG算子
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\grayBeauty.jpg');
    I=im2double(I);
    J=imnoise(I,'gaussian',0,0.005);
    [K,thresh]=edge(J,'log',[],2.3);%thresh为自动计算的分割阈值
    figure;
    subplot(121);imshow(J);
    subplot(122);imshow(K);
    
    %Kirsch算子
    close all;clear all;clc;
    I = imread('E:\Matlab_exercise\图片素材\lenn.jpg');
    figure;imshow(I,[]);title('原图');
    %对图像进行均值滤波
    I1 = filter2(fspecial('average',3),I);
    %对图像进行高斯滤波
    I2 = filter2(fspecial('gaussian'),I1);
    %利用小波变换对图像进行降噪处理
    [thr,sorh,keepapp] = ddencmp('den','wv',I2);%获得除燥的缺省参数
    I3 = wdencmp('gbl',I2,'sym4',2,thr,sorh,keepapp);%图像进行降噪处理
    
    %提取图像边缘
    t = [0.8 1.0 1.5 2.0 2.5].*10^5;%设定阈值
    J = double(I3);
    [m,n] = size(J);
    g = zeros(m,n);
    d = zeros(1,8);
    %利用Kirsch算子进行边缘提取
    for i=2:m-1;
        for j=2:n-1
           d(1) =(5*J(i-1,j-1)+5*J(i-1,j)+5*J(i-1,j+1)-3*J(i,j-1)+3*J(i,j+1)-3*J(i+1,j-1)-3*J(i+1,j)-3*J(i+1,j+1))^2; 
           d(2) =((-3)*J(i-1,j-1)+5*J(i-1,j)+5*J(i-1,j+1)-3*J(i,j-1)+5*J(i,j+1)-3*J(i+1,j-1)-3*J(i+1,j)-3*J(i+1,j+1))^2; 
           d(3) =((-3)*J(i-1,j-1)-3*J(i-1,j)+5*J(i-1,j+1)-3*J(i,j-1)+5*J(i,j+1)-3*J(i+1,j-1)-3*J(i+1,j)+5*J(i+1,j+1))^2; 
           d(4) =((-3)*J(i-1,j-1)-3*J(i-1,j)-3*J(i-1,j+1)-3*J(i,j-1)+5*J(i,j+1)-3*J(i+1,j-1)+5*J(i+1,j)+5*J(i+1,j+1))^2; 
           d(5) =((-3)*J(i-1,j-1)-3*J(i-1,j)-3*J(i-1,j+1)-3*J(i,j-1)-3*J(i,j+1)+5*J(i+1,j-1)+5*J(i+1,j)+5*J(i+1,j+1))^2; 
           d(6) =((-3)*J(i-1,j-1)-3*J(i-1,j)-3*J(i-1,j+1)+5*J(i,j-1)-3*J(i,j+1)+5*J(i+1,j-1)+5*J(i+1,j)-3*J(i+1,j+1))^2; 
           d(7) =(5*J(i-1,j-1)-3*J(i-1,j)-3*J(i-1,j+1)+5*J(i,j-1)-3*J(i,j+1)+5*J(i+1,j-1)-3*J(i+1,j)-3*J(i+1,j+1))^2; 
           d(8) =(5*J(i-1,j-1)+5*J(i-1,j)-3*J(i-1,j+1)+5*J(i,j-1)-3*J(i,j+1)-3*J(i+1,j-1)-3*J(i+1,j)-3*J(i+1,j+1))^2;      
           g(i,j) = max(d);
        end
    end
    
    %显示边缘提取后的图像
    for k=1:5
        for i=1:m
            for j=1:n
                if g(i,j)>t(k)
                    J(i,j)=255;
                else
                    J(i,j)=0;
                end
            end
        end
        figure;imshow(J,[]);
    end

③阈值分割技术

%灰度直方图分割
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\gray.jpg');
    J=I>120;
    [w,h]=size(I);
    for i=1:w
        for j=1:h
            if(I(i,j) > 130)
                K(i,j)=1;
            else
                K(i,j)=0;
            end
        end
    end
    figure;
    subplot(131);imshow(I);
    subplot(132);imshow(J);
    subplot(133);imshow(K);
    
    %彩色图像转换为二值图像
    close all;clear all;clc;
    [X,map]=imread('E:\Matlab_exercise\图片素材\trees.bmp');
    I=ind2gray(X,map);
    J=im2bw(X,map,0.4);%转化为二值图像
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);
    
    %OTSU阈值分割
    close all;clear all;clc;
    [X,map]=imread('E:\Matlab_exercise\图片素材\trees.bmp');
    I=ind2gray(X,map);
    I=im2double(I);
    T=graythresh(I);%OTSU阈值分割
    J=im2bw(I,T);
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);
    
    %迭代式阈值分割
    close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\grayBeauty.jpg');
    I=im2double(I);
    T0=0.01;
    T1=(min(I(:))+max(I(:)))/2;
    r1=find(I>T1);
    r2=find(I<=T1);
    T2=(mean(I(r1))+mean(I(r2)))/2;
    while abs(T2-T1)<T0
        T1=T2;
        r1=find(I>T1);
        r2=find(I<=T1);
        T2=(mean(I(r1))+mean(I(r2)))/2;%T2为最佳阈值
    end
    J=im2bw(I,T2);
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);

④区域分割技术

close all;clear all;clc;
    I=imread('E:\Matlab_exercise\图片素材\xianduan.jpg');
    I=rgb2gray(I);
    J=watershed(I,8);%8连通区域
    figure;
    subplot(121);imshow(I);
    subplot(122);imshow(J);