I have to do cross-correlation to prove two images , of the same scene and same size , are similar, if someone can help me , I would be grateful
MATLAB: How to implement cross-correlation to prove two images of the same scene are similar
image processingImage Processing Toolboxnormalized cross correlationpattern recognition
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Perhaps working through one of the examples in the xcorr documentation would be helpful.
1) why is it that there is still a peak r value with 2 spike trains that are not correlated?
You can cross correlate any two vectors and there will be a maximum correlation at some time point (unless both vectors are flat).
Produce this plot, for example.
theta = linspace(0,2*pi,100); x1 = sin(theta); x2 = sin(1.2*theta); [r, lags] = xcorr(x1,x2); clf()tiledlayout(2,1)nexttilehold onplot(theta, x1, 'DisplayName', 'x1')plot(theta, x2, 'DisplayName', 'x2')legend()nexttileplot(lags,r, 'ks')grid onxlabel('lag')ylabel('cross-corr')
2) Do I have to normalize the spike train?
You don't have to but it might be a good idea to normalize the amplitudes, especially if you're comparing responses from two different sources (ie, two different neurons). I also advise you to look at the literature from your field to see the common approach taken.
3) xcorr gives me the r and lag value. But how may I utilize this information to make it useful? ie is it significant?
After you get the lag, use circshift to circularly shift the 2nd vector so that it aligns to the first vector at the maximum correlation. Plot out the shifted data so you can visually confirm that the shift was done correctly. Then use [rho,pval] = corr(X,Y) to compute the correlation and the corresponding p-value for the shifted vectors.
Although often true, and maybe true for your signals, the best signal overlap is not guaranteed to occur when the cross correlation is maximum. That is a very common misconception. A simple thought experiment should reveal why. That is why I never use it and use normalized cross correlation instead. There is a function for that in the Image Processing Toolbox called normxcorr2(), however it should work for 1-D signals also I believe. I'm attaching a 2D pattern recognition demo (sorry I don't have any 1-D signal demo for it all ready to go).
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