Hello,
I have a 2D array of floating point values. Each row represents the profile of an object (the width in a given plane). Each object is sampled about 40 times - so 40 values per row, and we will end up with about 150 profiles. In other words it's not exactly a small array.
I need to compare these profiles with an incoming profile and return the best match for these values. Note that the floating point values will never match exactly.
I need to perform this comparison about 3 times per second, so I'm looking for a fast yet reliable solution.
I have found a couple posts here on the forum, which suggest simply running through each and every value and compare the variance. Though I haven't benchmarked it, I fear it may be resource intensive (and find it rather unelegant as well).
I have looked into CrossCorrelation.vi, which seems to be what I'm looking for. The problem there is that I'm not really sure what to make of the output from that function.
As far as I understand, the higher the values outputted from the Rxy terminal, the higher the deviation between the inputs. If that's correct, I could simply add up the Rxy values for each comparison and pick the one with the lowest value.
What I'm looking for is thoughts and ideas in general about my cross correlation solution (would it work at all?), and about any alternative solutions you might know of.
Thank you,
-Tobias
