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why are my cross correlation values so high?

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Heidi Hirsh
Heidi Hirsh on 26 Sep 2019
Commented: Heidi Hirsh on 5 Oct 2019
I am trying to find the best lag for relating to vectors of data (attached in the mat files as vectors x and y). I want to do a cross correlation to actually calculate the best lag. I am using xcorr to look at the cross correlation values ( c ) for different lags. lag= -5 has the highest value but the correlations are all over 3000. This seems really high. Can anyone explain this value to me. I've read the documentation for xcorr and I'm lost.
[c,lags] = xcor(x,y)
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Heidi Hirsh
Heidi Hirsh on 5 Oct 2019
No problem. I really appreciated your help! I am still not sure why the correlation outputs are so different between xcorr and crosscorr.

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Answers (2)

the cyclist
the cyclist on 26 Sep 2019
Edited: the cyclist on 26 Sep 2019
The values are right in line with what I would expect. A typical value would be
(typical x value) * (typical y value) * (length of vectors)
so
5 * 8 * 78 = 3120
See the "More About" section of the documentation.
If you were expecting it to be normalized, then look at the scaleopt input.
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Heidi Hirsh
Heidi Hirsh on 27 Sep 2019
Thank you! I did indeed want the normalized ('coeff') option!

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Image Analyst
Image Analyst on 27 Sep 2019
Cross correlation doesn't always have it's peak where the "lag" between two signals is, as a little thought will reveal. It shows you the sum of the multiplication of the overlapped terms as one signal slides past the other. For example if one signal has high values somewhere in one segment, but otherwise looks pretty much the same (just shifted), your peak correlation value won't be at the lag you think it should be. It might show you where the high values are, not where the bulk of the signal overlaps best.
There is another concept called normalized cross correlation you might want to look at. There is a 2-D version in the Image Processing Toolbox, normxcorr2(), and I attach an example for finding a template in a 2-D color image. I don't know if there is a 1-D version but often image processing functions will work on 1-D signals as well as 2-D signals.

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