数字图像处理|Kirsch边缘检测原理数字图像处理|数字图像处理

【数字图像处理|Kirsch边缘检测原理】
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%读出要处理的图象 clear clc close all bw=imread('e:\11.jpg'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %对图象进行预处理%画出原始图象 bw1=rgb2gray(bw); figure(1) imshow(bw1) title('原始图象')%对图象进行均值滤波处理 bw2=filter2(fspecial('average',3),bw1); figure(2) imshow(bw2) title('均值滤波')%对图象进行高斯滤波处理 bw3=filter2(fspecial('gaussian'),bw2); figure(3) imshow(bw3) title('高斯滤波')%利用小波变换对图象进行降噪处理 [thr,sorh,keepapp]=ddencmp('den','wv',bw3); %获得除噪的缺省参数 bw4=wdencmp('gbl',bw3,'sym4',2,thr,sorh,keepapp); %图象进行降噪处理 figure(4) imshow(bw4)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %提取图象边缘 t=1200 ; %设定阈值 colormap(gray(256)); %设定调色板 bw5=double(bw4); %把图变为十进制数 [m,n]=size(bw5); %得到图像的大小（长和宽） g=zeros(m,n); %定义一个大小为S的零矩阵 %利用Sobel算子进行边缘提取 for i=2:m-1 for j=2:n-1 d1 =(5*bw5(i-1,j-1)+5*bw5(i-1,j)+5*bw5(i-1,j+1)-3*bw5(i,j-1)-3*bw5(i,j+1)-3*bw5(i+1,j-1)-3*bw5(i+1,j)-3*bw5(i+1,j+1))^2; d2 =((-3)*bw5(i-1,j-1)+5*bw5(i-1,j)+5*bw5(i-1,j+1)-3*bw5(i,j-1)+5*bw5(i,j+1)-3*bw5(i+1,j-1)-3*bw5(i+1,j)-3*bw5(i+1,j+1))^2; d3 =((-3)*bw5(i-1,j-1)-3*bw5(i-1,j)+5*bw5(i-1,j+1)-3*bw5(i,j-1)+5*bw5(i,j+1)-3*bw5(i+1,j-1)-3*bw5(i+1,j)+5*bw5(i+1,j+1))^2; d4 =((-3)*bw5(i-1,j-1)-3*bw5(i-1,j)-3*bw5(i-1,j+1)-3*bw5(i,j-1)+5*bw5(i,j+1)-3*bw5(i+1,j-1)+5*bw5(i+1,j)+5*bw5(i+1,j+1))^2; d5 =((-3)*bw5(i-1,j-1)-3*bw5(i-1,j)-3*bw5(i-1,j+1)-3*bw5(i,j-1)-3*bw5(i,j+1)+5*bw5(i+1,j-1)+5*bw5(i+1,j)+5*bw5(i+1,j+1))^2; d6 =((-3)*bw5(i-1,j-1)-3*bw5(i-1,j)-3*bw5(i-1,j+1)+5*bw5(i,j-1)-3*bw5(i,j+1)+5*bw5(i+1,j-1)+5*bw5(i+1,j)-3*bw5(i+1,j+1))^2; d7 =(5*bw5(i-1,j-1)-3*bw5(i-1,j)-3*bw5(i-1,j+1)+5*bw5(i,j-1)-3*bw5(i,j+1)+5*bw5(i+1,j-1)-3*bw5(i+1,j)-3*bw5(i+1,j+1))^2; d8 =(5*bw5(i-1,j-1)+5*bw5(i-1,j)-3*bw5(i-1,j+1)+5*bw5(i,j-1)-3*bw5(i,j+1)-3*bw5(i+1,j-1)-3*bw5(i+1,j)-3*bw5(i+1,j+1))^2; g(i,j)=round(sqrt(d1+d2+d3+d4+d5+d6+d7+d8)); %梯度模取整 end end for i=1:m for j=1:n if g(i,j)>t bw5(i,j)=255; %将梯度值与阈值比较，大于T则把图像的灰度变为255，小于T则把图像的灰度变为0 else bw5(i,j)=0; end end end %显示边缘提取后的图象 figure(5) imshow(bw5) title('kirsch边缘检测')

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clc clear all close all A = imread('lena.jpg'); mask1=[-3,-3,-3; -3,0,5; -3,5,5]; % 建立方向模板 mask2=[-3,-3,5; -3,0,5; -3,-3,5]; mask3=[-3,5,5; -3,0,5; -3,-3,-3]; mask4=[-3,-3,-3; -3,0,-3; 5,5,5]; mask5=[5,5,5; -3,0,-3; -3,-3,-3]; mask6=[-3,-3,-3; 5,0,-3; 5,5,-3]; mask7=[5,-3,-3; 5,0,-3; 5,-3,-3]; mask8=[5,5,-3; 5,0,-3; -3,-3,-3]; B=mat2gray(A); subplot(121); imshow(B); title('原图'); I = im2double(A); % 将数据图像转化为双精度 d1 = imfilter(I, mask1); % 计算8个领域的灰度变化 d2 = imfilter(I, mask2); d3 = imfilter(I, mask3); d4 = imfilter(I, mask4); d5 = imfilter(I, mask5); d6 = imfilter(I, mask6); d7 = imfilter(I, mask7); d8 = imfilter(I, mask8); dd = max(abs(d1),abs(d2)); % 取差值变化最大的元素组成灰度变化矩阵 dd = max(dd,abs(d3)); dd = max(dd,abs(d4)); dd = max(dd,abs(d5)); dd = max(dd,abs(d6)); dd = max(dd,abs(d7)); dd = max(dd,abs(d8)); %grad = mat2gray(dd); % 将灰度变化矩阵转化为灰度图像 %level = graythresh(grad); % 计算灰度阈值 BW = im2bw(grad,0.03); % 用阈值分割梯度图像 subplot(122); imshow(BW); title('Kirsch算子的处理结果')% 显示分割后的图像，即边缘图像 title('Kirsch')

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