Avoiding FOR loops to select a region in matrix and minimise computation time

Question

Mohammed Manna 2012-10-18

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/51182-avoiding-for-loops-to-select-a-region-in-matrix-and-minimise-computation-time

Hello,

     I am trying to write a cross-correlation script where I need to slide my template across all rows and columns in the subject. Currently I am using nested FOR loops to select rows and columns every time I do a search. However, the computational time is quite high and I wanted to minimise it by avoiding FOR loops by any mean possible. For obvious reasons, I cannot publish my code yet. However, I can provide a pseudo code:
T = imread('template.png')
S = imread('subject.png')
[rowT, columnT] = size(T);
[rowS,columnS] = size(S);
coeff = zeros(size(S));  
for ii=0:(rowS-rowT)
    for jj = 0:(columnS - columnT)       
          ss = S(ii+1:rowT+ii,jj+1:columnT+jj);
          .
          .
          . 
          coeff(ii+1,jj+1) = (lambdaRo^2)*(lambdaGamma^2); 
      end
  end

Is there any way to avoid using for loops to select region of my subject image and speed the calculation time up? From my limited experience in Mathematics and MATLAB (compared to what the community users' experience is) I don't see how it is possible. Please help me out!!

Kind Regards,

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Mohammed Manna 2012-10-18

在 MATLAB Online 中打开

*Bjorn,

     Thanks for the response. Here is my script:*
    close all, clear all;
    clc;
    T = imread('T:/P7668/Manna/testTemplates/template_centred_1.png');
    % T = impyramid(T,'reduce');
    S = imread('T:/P7668/Manna/ssr_main_barrier/barrier_snapshot_1.png');  
    if size(S,3) > 1
        S = rgb2gray(S);
    end
    if size(T,3) > 1
        T = rgb2gray(T);
    end
    [rowT, columnT] = size(T);
    [rowS,columnS] = size(S);
    coeff = zeros(size(S));
    for ii=0:(rowS-rowT)
        for jj = 0:(columnS - columnT)        
            ss = S(ii+1:rowT+ii,jj+1:columnT+jj);
            rowProjectionS = sum(ss,2)/columnT;
            columnProjectionS = sum(ss,1)/rowT;
            rowProjectionT = sum(T,2)/columnT;
            columnProjectionT = sum(T,1)/rowT;
            meanT = mean(T(:));
            meanS = mean(ss(:));
            numeratorLambdaRo = sum((rowProjectionT-meanT).*(rowProjectionS - meanS));
            denominatorLambdaRo = sqrt( sum((rowProjectionT-meanT).^2) * sum((rowProjectionS-meanS).^2));
            lambdaRo = numeratorLambdaRo/denominatorLambdaRo;
            numeratorLambdaGamma = sum((columnProjectionT-meanT).*(columnProjectionS - meanS));
            denominatorLambdaGamma = sqrt( sum((columnProjectionT-meanT).^2) * sum((columnProjectionS-meanS).^2));
            lambdaGamma = numeratorLambdaGamma/denominatorLambdaGamma;
            coeff(ii+1,jj+1) = (lambdaRo^2)*(lambdaGamma^2); 
        end
    end

Image Analyst 2012-10-18

Again, why not use xcorr2()? Though, I admit I didn't study your code. Do you have some kind of unusual custom correlation that doesn't do the same thing as the built-in function?

Mohammed Manna 2012-11-9

Actually better!! I have got my own correlation implementation that is computationally a) cheap b) faster. I use cross-sectional histogram that is O(2n) rather than O(n^2). compared to normxcorr2 or xcorr2, it is about %20 faster...

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