亚像素边缘检测提取算法的实现
亚像素边缘检测提取算法的实现
其中包括插值法、拟合法、基于灰度矩法、基于zernike矩法等
通过Matlab软件运用zernike矩的检测算法
通过如下代码可实现
function zernike7(I)
I=imread(‘Pic1_3.bmp’);
% 7*7Zernike模板
M00=…
[
0 0.0287 0.0686 0.0807 0.0686 0.0287 0
0.0287 0.0815 0.0816 0.0816 0.0816 0.0815 0.0287
0.0686 0.0816 0.0816 0.0816 0.0816 0.0816 0.0686
0.0807 0.0816 0.0816 0.0816 0.0816 0.0816 0.0807
0.0686 0.0816 0.0816 0.0816 0.0816 0.0816 0.0686
0.0287 0.0815 0.0816 0.0816 0.0816 0.0815 0.0287
0 0.0287 0.0686 0.0807 0.0686 0.0287 0
];
M11R=…
[
0 -0.015 -0.019 0 0.019 0.015 0
-0.0224 -0.0466 -0.0233 0 0.0233 0.0466 0.0224
-0.0573 -0.0466 -0.0233 0 0.0233 0.0466 0.0573
-0.069 -0.0466 -0.0233 0 0.0233 0.0466 0.069
-0.0573 -0.0466 -0.0233 0 0.0233 0.0466 0.0573
-0.0224 -0.0466 -0.0233 0 0.0233 0.0466 0.0224
0 -0.015 -0.019 0 0.019 0.015 0
];
M11I=…
[
0 -0.0224 -0.0573 -0.069 -0.0573 -0.0224 0
-0.015 -0.0466 -0.0466 -0.0466 -0.0466 -0.0466 -0.015
-0.019 -0.0233 -0.0233 -0.0233 -0.0233 -0.0233 -0.019
0 0 0 0 0 0 0
0.019 0.0233 0.0233 0.0233 0.0233 0.0233 0.019
0.015 0.0466 0.0466 0.0466 0.0466 0.0466 0.015
0 0.0224 0.0573 0.069 0.0573 0.0224 0
];
M20=…
[
0 0.0225 0.0394 0.0396 0.0394 0.0225 0
0.0225 0.0271 -0.0128 -0.0261 -0.0128 0.0271 0.0225
0.0394 -0.0128 -0.0528 -0.0661 -0.0528 -0.0128 0.0394
0.0396 -0.0261 -0.0661 -0.0794 -0.0661 -0.0261 0.0396
0.0394 -0.0128 -0.0528 -0.0661 -0.0528 -0.0128 0.0394
0.0225 0.0271 -0.0128 -0.0261 -0.0128 0.0271 0.0225
0 0.0225 0.0394 0.0396 0.0394 0.0225 0
];
if length(size(I))==3 I=rgb2gray(I); end
I=im2bw(I,0.6);
K=double(I);
[m n]=size(K);
xs=double(zeros(m,n));
ys=double(zeros(m,n));
% 卷积运算
A11I=conv2(M11I,K);
A11R=conv2(M11R,K);
A20=conv2(M20,K);
% 截掉多余部分
A11I=A11I(4:end-3,4:end-3);
A11R=A11R(4:end-3,4:end-3);
A20=A20(4:end-3,4:end-3);
J=zeros(size(K));
boundary=J;
theta=atan2(A11I,A11R);%计算theta
%计算边缘的三个参数
A11C=A11R.*cos(theta)+A11I.sin(theta);
l=A20./A11C;
k=1.5A11C./((1-l.2).1.5);
e=abs(l)>1/3.5;
k(e)=0;
%边缘判断条件
a=abs(l)<1/sqrt(2)*2/7;
b=abs(k)>max(I()/10;
% a,b分别为距离和边缘强度判断结果
J(a&b)=1;
%将图像的最边缘去除
% boundary(2:end-1,2:end-1)=1;
% J(~boundary)=0;
format short
% [x,y]=find(J1);%边缘的像素级坐标
% O=[x y];
% Z=[x+l(find(J1)).*cos(theta(find(J1))) y+l(find(J1)).*sin(theta(find(J==1)))];%亚像素坐标
% % fprintf(’%.4f %.4f\n’,Z’);
[L,num]=bwlabel(J,8);%对二值图像进行标记
%自动化搜索连通域
s=zeros(1,num);
for i=1:num
s(i)=size(find(L==i),1);
end
[bwL,label]=sort(s,‘descend’);
if label(1)
index2=label(2);
else
index1=label(2);
index2=label(1);
end
%计算左边探针的最前端坐标
[r1,c1]=find(L==index1);
A1=[r1 c1];
y1=max(A1(:,2));%该连通域中y最大值为针尖处
x1=max(A1(find(A1(:,2)==y1),1));
x1sub=x1+3.5*l(x1,y1)cos(theta(x1,y1));
y1sub=y1+3.5l(x1,y1)*sin(theta(x1,y1));
%计算最右边探针的最前端坐标
[r2,c2]=find(L==index2);
A2=[r2 c2];
y2=min(A2(:,2));%该连通域中y最小为连通域
x2=max(A2(find(A2(:,2)==y2),1));
x2sub=x2+3.5*l(x2,y2)cos(theta(x2,y2));
y2sub=y2+3.5l(x2,y2)*sin(theta(x2,y2));
% [x1sub y1sub],[x2sub,y2sub]
subplot(221)
imshow(J)
% figure;
% imcontour(J,1)
% 边界提取
% subplot(122);
% bwimg = bwmorph(J,‘remove’);
% imshow(bwimg)
subplot(222);
I41=imfill(J,‘holes’);
imshow(I41)
title(‘孔洞填充图像’);
% 提取最外围边缘
subplot(223);
I4=bwperim(I41);
imshow(I4); title(‘边缘图像’);
% 去除面积小于150px物体
subplot(224);
I5=bwareaopen(I4,100);
imshow(I5);