%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% a: model RGB image %
% b: target RGB image %
% c: output the match image %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ c ] = edge_match( a,b)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%用sobel掩膜提取边缘并保留边缘点的梯度方向与ang数组中%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ma=im2double(rgb2gray(a)); %样本图像
mb=im2double(rgb2gray(b)); %模板
%水平与竖直方向的sobel掩膜
mask_Ver= fspecial('sobel'); %垂直方向上的掩膜
mask_Lev= mask_Ver'; %水平方向掩膜
%图像在水平方向和垂直方向上对sobel算子的响应
imgSobel_Lev=imfilter(ma,double(mask_Lev),'corr','replicate','same');
imgSobel_Ver=imfilter(ma,double(mask_Ver),'corr','replicate','same');
%imwrite(imgSobel_Ver,'Ver.jpg');
%imwrite(imgSobel_Lev,'Lev.jpg');
%%
%计算模板特征点角度
[Row,Col]=size(ma);
for i=1:Row
for j=1:Col
imgEdge(i,j)=sqrt(imgSobel_Ver(i,j)*imgSobel_Ver(i,j)+imgSobel_Lev(i,j)*imgSobel_Lev(i,j));
if(imgSobel_Lev(i,j)==0)
temp=0;
elseif(imgSobel_Ver(i,j)==0)
temp=90.0;
else
temp =atan(imgSobel_Lev(i,j)/imgSobel_Ver(i,j));
end
if(temp<0)
temp=180*(pi+temp)/(pi);
else
temp=180*temp/(pi);
end
%将角度归为0,45,90,135,四个方向
if((temp<22.5)||(temp>157.5))
ang(i,j)=0;
elseif(temp>=22.5&&temp<=67.5)
ang(i,j)=45;
elseif(temp>67.5&&temp<112.5)
ang(i,j)=90;
elseif(temp>=112.5&&temp<=157.5)
ang(i,j)=135;
end
end
end
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%非最大抑制法% && %孤立点删除%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
avg=0;%边缘点强度的平局值
count=0;%边缘点个数
%摒弃图像边缘点
for i=1:Row
for j=1:Col
if(i==1 || i==Row || j==1 || j==Col )
imgEdge(i,j)=0;
else
%最大抑制法
if(imgEdge(i,j)~=0)%是边缘点
switch ang(i,j)
case 0
ptLeft=imgEdge(i,j-1);
ptRight=imgEdge(i,j+1);
case 45
ptLeft=imgEdge(i-1,j+1);
ptRight=imgEdge(i+1,j-1);
case 90
ptLeft=imgEdge(i-1,j);
ptRight=imgEdge(i+1,j);
case 135
ptLeft=imgEdge(i-1,j-1);
ptRight=imgEdge(i+1,j+1);
end %switch
if(imgEdge(i,j)<ptLeft||imgEdge(i,j)<ptRight)
imgEdge(i,j)=0;
imgSobel_Ver(i,j)=0;
imgSobel_Lev(i,j)=0;
else
avg=avg+imgEdge(i,j);
count=count+1;
end
end %if(imgEdge(i,j)~=0)
end %end of if(i==1)
end %end of Col
end%end of Row
%%
%对比度抑制
avg=avg/count;
%重心坐标
gravity_x=0;
gravity_y=0;
MagG=0;
count=0;
maxContrast = avg*0.8;%满足最大的肯定是边缘点
minContrast = avg*0.5;%低于最小的肯定不是
for i=2:Row-1
for j=2:Col-1
%上下门限删除孤立点
tl=imgEdge(i-1,j-1);
tp=imgEdge(i-1,j);
tr=imgEdge(i-1,j+1);
lt=imgEdge(i,j-1);
rt=imgEdge(i,j+1);
bl=imgEdge(i+1,j-1);
bt=imgEdge(i+1,j);
br=imgEdge(i+1,j+1);
if(imgEdge(i,j)~=0 && imgEdge(i,j)<maxContrast)
%删除对比度过小的边缘点
if(imgEdge(i,j)<minContrast)
imgEdge(i,j)=0;
imgSobel_Ver(i,j)=0;
imgSobel_Lev(i,j)=0;
%如果八邻域都没有绝对边缘点，认为是孤立点，删除
elseif(tl<maxContrast&&tp<maxContrast&&tr<maxContrast&&...
lt<maxContrast&&rt<maxContrast&&...
bl<maxContrast&&bt<maxContrast&&br<maxContrast)
imgEdge(i,j)=0;
imgSobel_Ver(i,j)=0;
imgSobel_Lev(i,j)=0;
end %if(imgEdge(i,j)<minContrast)
end %if(imgEdge(i,j)<maxContrast)
%%
%求重心
if(imgEdge(i,j)~=0)%((imgSobel_Ver(i,j)~=0||imgSobel_Lev(i,j)~=0&&imgEdge(i,j)~=0))
gravity_x=gravity_x+i*imgEdge(i,j);
gravity_y=gravity_y+j*imgEdge(i,j);
MagG=MagG+imgEdge(i,j);
%记录模板特征描述符
count=count+1;
Model_mag(count)= 1/imgEdge(i,j);
Model_y(count)=j;
Model_x(count)=i;
ModDerivative_Ver(count)=imgSobel_Ver(i,j);
ModDerivative_Lev(count)=imgSobel_Lev(i,j);
end % end of (imgEdge(i,j)~=0
end % end of col
end % end of row
% imwrite(imgEdge,'edge3.jpg');
% imwrite(imgSobel_Ver,'imgSobel_Ver.jpg');
% imwrite(imgSobel_Lev,'imgSobel_Lev.jpg');
%%%%%%%%%%%%%%%%%%计算模板特征描述%%%%%%%%%%%%%%%%%%%%%
%%
gravity_x=int32(gravity_x/MagG);
gravity_y=int32(gravity_y/MagG);
ModFeatNo=count;%模板的特征点数量
%将特征点位置描述改为以重心为原心
for k=1:ModFeatNo
Model_x(k)=Model_x(k)-gravity_x;
Model_y(k)=Model_y(k)-gravity_y;
end
%%%%%%%%%%%%%%%%%%匹配算法%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
[Row,Col]=size(mb);%待测目标
%图像在水平方向和垂直方向上对sobel算子的响应
tagSobel_Lev=imfilter(mb,double(mask_Lev),'corr','replicate','same');
tagSobel_Ver=imfilter(mb,double(mask_Ver),'corr','replicate','same');
targDeriv_Ver=0;
targDeriv_Lev=0;
modDeriv_Ver=0;
modDeriv_Lev=0;
score=0;%匹配得分
BestScore=0;
location=[-1,-1];%模板重心在目标图像的位置
minScore =0.1 ;%最低匹配得分，低于最低认为匹配失败
normMinScore=minScore/ModFeatNo;
greediness=0.01;%模板的梯度指数，
normGreediness=(1-greediness*minScore)/(1-greediness);
patialSum=0;%局部最大值
for i=1:Row
for j=1:Col
for k=1:ModFeatNo
CurX=Model_x(k)+i;
CurY=Model_y(k)+j;
%游标位置限制，待测目标的搜索起始和结束位置不会使得模板越界
if(CurX<1 || CurY<1 || CurX> Row || CurY> Col)
continue;
else
%相似性判别
targDeriv_Ver=tagSobel_Ver(CurX,CurY);
targDeriv_Lev=tagSobel_Lev(CurX,CurY);
if((targDeriv_Ver~=0 || targDeriv_Lev~=0))
modDeriv_Ver=ModDerivative_Ver(k);
modDeriv_Lev=ModDerivative_Lev(k);
targMag=sqrt(targDeriv_Ver*targDeriv_Ver+targDeriv_Lev*targDeriv_Lev);
if(targMag~=0)
targMag=1/targMag;
end;
%余弦向量相似性度量
patialSum =patialSum +((modDeriv_Ver*targDeriv_Ver) +...
(modDeriv_Lev*targDeriv_Lev))*(Model_mag(k)*targMag);
end
%退出判断
thres=[(minScore-1)+normGreediness*k,normMinScore*k];
threshold=min(thres);
score=patialSum;
if(score<threshold)
break;
end
end %if(CurX<1 || CurY<1）
end %for k=1:ModFeatNo
score=patialSum/ModFeatNo;
patialSum=0;
if(score>=BestScore)
BestScore=score;
location(1)=i;
location(2)=j;
end % if(score>=BestScore)
end %for j=1:Col
end %for i=1:Row
%%
%模板在目标图像中的坐标
[Row,Col]=size(ma);
countt=1;
location(1);
location(2);
for i=1:Row;
for j=1:Col;
if(imgEdge(i,j)~=0)
x1(countt)=location(1)+i-gravity_x;
y1(countt)=location(2)+j-gravity_y;
countt=countt+1;
end
end
end
%%
%画出输出图像
figure
hold on;
image(b);
plot(y1,x1,'o','LineWidth',2,... %设置圆圈的线粗
'MarkerEdgeColor','r',... %边界设置为黑色
'MarkerFaceColor','r',... %内部设置白色
'MarkerSize',2) %大小设置
hold off;
c=b;
%%%%%%%最后一个end
end
模板：
目标：
结果：
