基于边缘的模板匹配

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                                                          %
%   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) 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=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:

基于边缘的模板匹配_第1张图片

结果:

基于边缘的模板匹配_第2张图片

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