matlab图像分割算法源码要点计划matlab图像切割算法源码/32图像读取及灰度变换I=imread(cameraman.tif);%subplot(1,2,1),imshow(I)%读取图像输出图像title(原始图像)%subplot(1,2,2),imhist(I)%title(原始图像直方图在原始图像中加标题输出原图直方图)%在原图直方图上加标题图像旋转I=imread(cameraman.tif);figure,imshow(I);theta=30;K=imrotate(I,theta);%Tryvaryingtheangle,theta.figure,imshow(K)边沿检测I=imread(cameraman.tif);J1=edge(I,sobel);J2=edge(I,prewitt);J3=edge(I,log);subplot(1,4,1),imshow(I);subplot(1,4,2),imshow(J1);subplot(1,4,3),imshow(J2);subplot(1,4,4),imshow(J3);1.图像反转MATLAB程序实现以下：I=imread(xian.bmp);J=double(I);J=-J+(256-1);%图像反转线性变换H=uint8(J);subplot(1,2,1),imshow(I);subplot(1,2,2),imshow(H);2.灰度线性变换MATLAB程序实现以下：I=imread(xian.bmp);subplot(2,2,1),imshow(I);title(原始图像);axis(50,250,50,200);axison;%显示坐标系I1=rgb2gray(I);subplot(2,2,2),imshow(I1);title(灰度图像);axis(50,250,50,200);axison;%显示坐标系J=imadjust(I1,0.10.5,);%局部拉伸，把0.10.5内的灰度拉伸为01subplot(2,2,3),imshow(J);title(线性变换图像0.10.5);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系K=imadjust(I1,0.30.7,);%局部拉伸，把0.30.7内的灰度拉伸为01subplot(2,2,4),imshow(K);title(线性变换图像0.30.7);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系3.非线性变换MATLAB程序实现以下：I=imread(xian.bmp);I1=rgb2gray(I);subplot(1,2,1),imshow(I1);title(灰度图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系J=double(I1);J=40*(log(J+1);H=uint8(J);subplot(1,2,2),imshow(H);title(对数变换图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系4.直方图均衡化MATLAB程序实现以下：I=imread(xian.bmp);I=rgb2gray(I);figure;subplot(2,2,1);imshow(I);subplot(2,2,2);imhist(I);I1=histeq(I);figure;subplot(2,2,1);imshow(I1);subplot(2,2,2);imhist(I1);5.线性光滑滤波器用MATLAB实现领域均匀法克制噪声程序：I=imread(xian.bmp);subplot(231)imshow(I)title(原始图像)I=rgb2gray(I);I1=imnoise(I,salt&pepper,0.02);subplot(232)imshow(I1)title(增加椒盐噪声的图像)k1=filter2(fspecial(average,3),I1)/255;%进行3*3模板光滑滤波k2=filter2(fspecial(average,5),I1)/255;%进行5*5模板光滑滤波k3=filter2(fspecial(average,7),I1)/255;%进行7*7模板光滑滤波k4=filter2(fspecial(average,9),I1)/255;%进行9*9模板光滑滤波subplot(233),imshow(k1);title(3*3模板光滑滤波);subplot(234),imshow(k2);title(5*5模板光滑滤波);subplot(235),imshow(k3);title(7*7模板光滑滤波);subplot(236),imshow(k4);title(9*9模板光滑滤波);6.中值滤波器用MATLAB实现中值滤波程序以下：I=imread(xian.bmp);I=rgb2gray(I);J=imnoise(I,salt&pepper,0.02);subplot(231),imshow(I);title(原图像);subplot(232),imshow(J);title(增加椒盐噪声图像);k1=medfilt2(J);%进行3*3模板中值滤波k2=medfilt2(J,5,5);%进行5*5模板中值滤波k3=medfilt2(J,7,7);%进行7*7模板中值滤波k4=medfilt2(J,9,9);%进行9*9模板中值滤波subplot(233),imshow(k1);title(3*3模板中值滤波);subplot(234),imshow(k2);title(5*5模板中值滤波);subplot(235),imshow(k3);title(7*7模板中值滤波);subplot(236),imshow(k4);title(9*9模板中值滤波);7.用Sobel算子和拉普拉斯对图像锐化：I=imread(xian.bmp);subplot(2,2,1),imshow(I);title(原始图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系I1=im2bw(I);subplot(2,2,2),imshow(I1);title(二值图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系H=fspecial(sobel);%选择sobel算子J=filter2(H,I1);%卷积运算subplot(2,2,3),imshow(J);title(sobel算子锐化图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系h=010,1-41,010;%拉普拉斯算子J1=conv2(I1,h,same);%卷积运算subplot(2,2,4),imshow(J1);title(拉普拉斯算子锐化图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系8.梯度算子检测边沿用MATLAB实现以下：I=imread(xian.bmp);subplot(2,3,1);imshow(I);title(原始图像);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系I1=im2bw(I);subplot(2,3,2);imshow(I1);title(二值图像);axis(50,250,50,200);gridon;%显示网格线axison;I2=edge(I1,roberts);figure;subplot(2,3,3);imshow(I2);%显示坐标系title(roberts算子切割结果);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系I3=edge(I1,sobel);subplot(2,3,4);imshow(I3);title(sobel算子切割结果);axis(50,250,50,200);gridon;%显示网格线axison;I4=edge(I1,Prewitt);subplot(2,3,5);imshow(I4);%显示坐标系title(Prewitt算子切割结果);axis(50,250,50,200);gridon;%显示网格线axison;%显示坐标系9.LOG算子检测边沿用MATLAB程序实现以下：I=imread(xian.bmp);subplot(2,2,1);imshow(I);title(原始图像);I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title(灰度图像);I2=edge(I1,log);subplot(2,2,3);imshow(I2);title(log算子切割结果);10.Canny算子检测边沿用MATLAB程序实现以下：I=imread(xian.bmp);subplot(2,2,1);imshow(I);title(原始图像)I1=rgb2gray(I);subplot(2,2,2);imshow(I1);title(灰度图像);I2=edge(I1,canny);subplot(2,2,3);imshow(I2);title(canny算子切割结果);11.界限追踪（bwtraceboundary函数）clcclearallI=imread(xian.bmp);figureimshow(I);title(原始图像);I1=rgb2gray(I);%将彩色图像转变灰度图像threshold=graythresh(I1);%计算将灰度图像转变成二值图像所需的门限BW=im2bw(I1,threshold);%将灰度图像转变成二值图像figureimshow(BW);title(二值图像);dim=size(BW);col=round(d