SSD算法 模板 匹配
SSD算法
误差平方和算法（Sum of Squared Differences，简称SSD算法），也叫差方和算法。实际上，SSD算法与SAD算法如出一辙，只是其相似度测量公式有一点改动（计算的是子图与模板图的L2距离）。这里不再赘述。
matlab 代码：
<pre name="code" class="cpp">%%
%绝对误差和算法（SAD）
clear all;
close all;
%%
src=imread('lena.jpg');
[a b d]=size(src);
if d==3
src=rgb2gray(src);
end
mask=imread('lena_mask.jpg');
[m n d]=size(mask);
if d==3
mask=rgb2gray(mask);
end
%%
N=n;%模板尺寸，默认模板为正方形
M=a;%代搜索图像尺寸，默认搜索图像为正方形
%%
dst=zeros(M-N,M-N);
for i=1:M-N %行
for j=1:M-N
temp=src(i:i+N-1,j:j+N-1);
dst(i,j)=dst(i,j)+sum(sum((temp-mask).^2));
end
end
abs_min=min(min(dst));
[x,y]=find(dst==abs_min);
figure;
imshow(mask);title('模板');
figure;
imshow(src);
hold on;
rectangle('position',[x,y,N-1,N-1],'edgecolor','r');
hold off;title('搜索图');
或者：
利用频域的变化
function [I_SSD,I_NCC,Idata]=template_matching(T,I,IdataIn)
% TEMPLATE_MATCHING is a cpu efficient function which calculates matching
% score images between template and an (color) 2D or 3D image.
% It calculates:
% - The sum of squared difference (SSD Block Matching), robust template
% matching.
% - The normalized cross correlation (NCC), independent of illumination,
% only dependent on texture
% The user can combine the two images, to get template matching which
% works robust with his application.
% Both measures are implemented using FFT based correlation.
%
% [I_SSD,I_NCC,Idata]=template_matching(T,I,Idata)
%
% inputs,
% T : Image Template, can be grayscale or color 2D or 3D.
% I : Color image, can be grayscale or color 2D or 3D.
% (optional)
% Idata : Storage of temporary variables from the image I, to allow
% faster search for multiple templates in the same image.
%
% outputs,
% I_SSD: The sum of squared difference 2D/3D image. The SSD sign is
% reversed and normalized to range [0 1]
% I_NCC: The normalized cross correlation 2D/3D image. The values
% range between 0 and 1
% Idata : Storage of temporary variables from the image I, to allow
% faster search for multiple templates in the same image.
%
% Example 2D,
% % Find maximum response
% I = im2double(imread('lena.jpg'));
% % Template of Eye Lena
% T=I(124:140,124:140,:);
% % Calculate SSD and NCC between Template and Image
% [I_SSD,I_NCC]=template_matching(T,I);
% % Find maximum correspondence in I_SDD image
% [x,y]=find(I_SSD==max(I_SSD(:)));
% % Show result
% figure,
% subplot(2,2,1), imshow(I); hold on; plot(y,x,'r*'); title('Result')
% subplot(2,2,2), imshow(T); title('The eye template');
% subplot(2,2,3), imshow(I_SSD); title('SSD Matching');
% subplot(2,2,4), imshow(I_NCC); title('Normalized-CC');
%
% Example 3D,
% % Make some random data
% I=rand(50,60,50);
% % Get a small volume as template
% T=I(20:30,20:30,20:30);
% % Calculate SDD between template and image
% I_SSD=template_matching(T,I);
% % Find maximum correspondence
% [x,y,z]=ind2sub(size(I_SSD),find(I_SSD==max(I_SSD(:))));
% disp(x);
% disp(y);
% disp(z);
%
% Function is written by D.Kroon University of Twente (February 2011)
if(nargin<3), IdataIn=[]; end
% Convert images to double
T=double(T); I=double(I);
if(size(T,3)==3)
% Color Image detected
[I_SSD,I_NCC,Idata]=template_matching_color(T,I,IdataIn);
else
% Grayscale image or 3D volume
[I_SSD,I_NCC,Idata]=template_matching_gray(T,I,IdataIn);
end
function [I_SSD,I_NCC,Idata]=template_matching_color(T,I,IdataIn)
if(isempty(IdataIn)), IdataIn.r=[]; IdataIn.g=[]; IdataIn.b=[]; end
% Splite color image, and do template matching on R,G and B image
[I_SSD_R,I_NCC_R,Idata.r]=template_matching_gray(T(:,:,1),I(:,:,1),IdataIn.r);
[I_SSD_G,I_NCC_G,Idata.g]=template_matching_gray(T(:,:,2),I(:,:,2),IdataIn.g);
[I_SSD_B,I_NCC_B,Idata.b]=template_matching_gray(T(:,:,3),I(:,:,3),IdataIn.b);
% Combine the results
I_SSD=(I_SSD_R+I_SSD_G+I_SSD_B)/3;
I_NCC=(I_NCC_R+I_NCC_G+I_NCC_B)/3;
function [I_SSD,I_NCC,Idata]=template_matching_gray(T,I,IdataIn)
% Calculate correlation output size = input size + padding template
T_size = size(T); I_size = size(I);%模板和图像的大小
outsize = I_size + T_size-1;%这个是扩展边界后的大小
% calculate correlation in frequency domain
if(length(T_size)==2)
FT = fft2(rot90(T,2),outsize(1),outsize(2));%计算模板的的频域
if(isempty(IdataIn))
Idata.FI = fft2(I,outsize(1),outsize(2));%计算图像的频域
else
Idata.FI=IdataIn.FI;
end
Icorr = real(ifft2(Idata.FI.* FT));%进行反变换 时域卷积，频域相乘 得到二者的相关性
else
FT = fftn(rot90_3D(T),outsize);
FI = fftn(I,outsize);
Icorr = real(ifftn(FI.* FT));
end
% Calculate Local Quadratic sum of Image and Template
if(isempty(IdataIn))
Idata.LocalQSumI= local_sum(I.*I,T_size);%计算图像的局部的差的平方
else
Idata.LocalQSumI=IdataIn.LocalQSumI;
end
QSumT = sum(T(:).^2);%模板中全部像素的和平方
% SSD between template and image
I_SSD=Idata.LocalQSumI+QSumT-2*Icorr;
% Normalize to range 0..1
I_SSD=I_SSD-min(I_SSD(:));
I_SSD=1-(I_SSD./max(I_SSD(:)));
% Remove padding
I_SSD=unpadarray(I_SSD,size(I));
if (nargout>1)
% Normalized cross correlation STD
if(isempty(IdataIn))
Idata.LocalSumI= local_sum(I,T_size);%计算出图像中模板快内的像素之和
else
Idata.LocalSumI=IdataIn.LocalSumI;
end
% Standard deviation
if(isempty(IdataIn))
Idata.stdI=sqrt(max(Idata.LocalQSumI-(Idata.LocalSumI.^2)/numel(T),0) );%numl元素的个数
else
Idata.stdI=IdataIn.stdI;
end
stdT=sqrt(numel(T)-1)*std(T(:));
% Mean compensation
meanIT=Idata.LocalSumI*sum(T(:))/numel(T);
I_NCC= 0.5+(Icorr-meanIT)./ (2*stdT*max(Idata.stdI,stdT/1e5));
% Remove padding
I_NCC=unpadarray(I_NCC,size(I));
end
function T=rot90_3D(T)
T=flipdim(flipdim(flipdim(T,1),2),3);
function B=unpadarray(A,Bsize)
Bstart=ceil((size(A)-Bsize)/2)+1;
Bend=Bstart+Bsize-1;
if(ndims(A)==2)
B=A(Bstart(1):Bend(1),Bstart(2):Bend(2));
elseif(ndims(A)==3)
B=A(Bstart(1):Bend(1),Bstart(2):Bend(2),Bstart(3):Bend(3));
end
function local_sum_I= local_sum(I,T_size)
% Add padding to the image
B = padarray(I,T_size);%对图像进行填充
% Calculate for each pixel the sum of the region around it,
% with the region the size of the template. 计算模板区域内的和 就是SSD
if(length(T_size)==2)
% 2D localsum
s = cumsum(B,1);
c = s(1+T_size(1):end-1,:)-s(1:end-T_size(1)-1,:);
s = cumsum(c,2);
local_sum_I= s(:,1+T_size(2):end-1)-s(:,1:end-T_size(2)-1);
else
% 3D Localsum
s = cumsum(B,1);
c = s(1+T_size(1):end-1,:,:)-s(1:end-T_size(1)-1,:,:);
s = cumsum(c,2);
c = s(:,1+T_size(2):end-1,:)-s(:,1:end-T_size(2)-1,:);
s = cumsum(c,3);
local_sum_I = s(:,:,1+T_size(3):end-1)-s(:,:,1:end-T_size(3)-1);
end