目录

代码和图片下载

1 边缘检测理论

【参考文献】：亚像素边缘检测技术研究_张美静

2 MATLAB实现

function zernike7(I)I=imread('Pic1_3.bmp');subplot(221)imshow(I)title('原图像');% 7*7Zernike模板M00=...[00.02870.06860.08070.06860.028700.02870.08150.08160.08160.08160.08150.02870.06860.08160.08160.08160.08160.08160.06860.08070.08160.08160.08160.08160.08160.08070.06860.08160.08160.08160.08160.08160.06860.02870.08150.08160.08160.08160.08150.028700.02870.06860.08070.06860.02870];M11R=...[0-0.015-0.01900.0190.015 0-0.0224-0.0466-0.023300.02330.04660.0224-0.0573-0.0466-0.023300.02330.04660.0573-0.069-0.0466-0.023300.02330.04660.069-0.0573-0.0466-0.023300.02330.04660.0573-0.0224-0.0466-0.023300.02330.04660.02240-0.015-0.01900.0190.015 0];M11I=...[0-0.0224-0.0573-0.069-0.0573-0.0224 0-0.015-0.0466-0.0466-0.0466-0.0466-0.0466-0.015-0.019-0.0233-0.0233-0.0233-0.0233-0.0233-0.0190 0 0 0 0 0 00.0190.02330.02330.02330.02330.02330.0190.0150.04660.04660.04660.04660.04660.01500.02240.05730.0690.05730.0224 0];M20=...[00.02250.03940.03960.03940.022500.02250.0271-0.0128-0.0261-0.01280.02710.02250.0394-0.0128-0.0528-0.0661-0.0528-0.01280.03940.0396-0.0261-0.0661-0.0794-0.0661-0.02610.03960.0394-0.0128-0.0528-0.0661-0.0528-0.01280.03940.02250.0271-0.0128-0.0261-0.01280.02710.022500.02250.03940.03960.03940.02250];if length(size(I))==3 I=rgb2gray(I); endI=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=1.5*A11C./((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(J==1);%边缘的像素级坐标% O=[x y];% Z=[x+l(find(J==1)).*cos(theta(find(J==1))) y+l(find(J==1)).*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:nums(i)=size(find(L==i),1);end[bwL,label]=sort(s,'descend');if label(1)<label(2)index1=label(1);index2=label(2);elseindex1=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+3.5*l(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+3.5*l(x2,y2)*sin(theta(x2,y2));% [x1sub y1sub],[x2sub,y2sub]subplot(222)imshow(J)title('检测结果图像');% figure;% imcontour(J,1)% 边界提取% subplot(122);% bwimg = bwmorph(J,'remove');% imshow(bwimg)subplot(223);I41=imfill(J,'holes');imshow(I41)title('孔洞填充图像');% 提取最外围边缘subplot(224);I4=bwperim(I41); imshow(I4); title('提取外边缘图像');% 去除面积小于150px物体% subplot(224); % I5=bwareaopen(I4,100); % imshow(I5);% title('去除小面积边缘图像');

（1）实验图

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