SSD算法

? ? ? ? 誤差平方和算法（Sum of Squared Differences，簡稱SSD算法），也叫差方和算法。實(shí)際上，SSD算法與SAD算法如出一轍，只是其相似度測量公式有一點(diǎn)改動（計(jì)算的是子圖與模板圖的L2距離）。這里不再贅述。

matlab 代碼：

<pre name="code" class="cpp">%% %絕對誤差和算法（SAD） clear all; close all; %% src=imread('lena.jpg'); [a b d]=size(src); if d==3src=rgb2gray(src); end mask=imread('lena_mask.jpg'); [m n d]=size(mask); if d==3mask=rgb2gray(mask); end %% N=n;%模板尺寸，默認(rèn)模板為正方形 M=a;%代搜索圖像尺寸，默認(rèn)搜索圖像為正方形 %% dst=zeros(M-N,M-N); for i=1:M-N %行for j=1:M-Ntemp=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('搜索圖');

或者：

利用頻域的變化

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); endfunction [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);%模板和圖像的大小 outsize = I_size + T_size-1;%這個是擴(kuò)展邊界后的大小% calculate correlation in frequency domain if(length(T_size)==2)FT = fft2(rot90(T,2),outsize(1),outsize(2));%計(jì)算模板的的頻域if(isempty(IdataIn))Idata.FI = fft2(I,outsize(1),outsize(2));%計(jì)算圖像的頻域elseIdata.FI=IdataIn.FI;endIcorr = real(ifft2(Idata.FI.* FT));%進(jìn)行反變換 時域卷積，頻域相乘 得到二者的相關(guān)性 elseFT = 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);%計(jì)算圖像的局部的差的平方 elseIdata.LocalQSumI=IdataIn.LocalQSumI; endQSumT = 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 STDif(isempty(IdataIn))Idata.LocalSumI= local_sum(I,T_size);%計(jì)算出圖像中模板快內(nèi)的像素之和elseIdata.LocalSumI=IdataIn.LocalSumI;end% Standard deviationif(isempty(IdataIn))Idata.stdI=sqrt(max(Idata.LocalQSumI-(Idata.LocalSumI.^2)/numel(T),0) );%numl元素的個數(shù)elseIdata.stdI=IdataIn.stdI;endstdT=sqrt(numel(T)-1)*std(T(:));% Mean compensationmeanIT=Idata.LocalSumI*sum(T(:))/numel(T);I_NCC= 0.5+(Icorr-meanIT)./ (2*stdT*max(Idata.stdI,stdT/1e5));% Remove paddingI_NCC=unpadarray(I_NCC,size(I)); endfunction 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)); endfunction local_sum_I= local_sum(I,T_size) % Add padding to the image B = padarray(I,T_size);%對圖像進(jìn)行填充% Calculate for each pixel the sum of the region around it, % with the region the size of the template. 計(jì)算模板區(qū)域內(nèi)的和 就是SSD if(length(T_size)==2)% 2D localsums = 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 Localsums = 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

《新程序員》：云原生和全面數(shù)字化實(shí)踐50位技術(shù)專家共同創(chuàng)作，文字、視頻、音頻交互閱讀

總結(jié)

以上是生活随笔為你收集整理的SSD算法 模板 匹配的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。