【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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