cross-correlation

My boss and I disagree as to what is going on with the CV_TM_CCORR_NORMED method for matchTemplate(); in openCV. Can you please explain what is happening here especially the square root aspect of this equation. Correlation is similarity of two signals,vectors etc. Suppose you have vectors template=[0 1 0 0 1 0 ] A=[0 1 1 1 0 0] B =[ 1 0 0 0 0 1] if you perform correlation between vectors and template to get which one is more similar ,you will see A is similar to template more than B because 1's are placed in corresponding indexes.This means the more nonzero elements corresponds the more

Possible Duplicate: Matlab Cross correlation vs Correlation Coefficient question When I cross correlate 2 data sets a and b (each 73 points long) in MATLAB and graph it, it appears like a triangle with 145 points. I'm confused between the correlation coefficient and the triangle-like graph when I plot the cross correlation output which ranges from +/- 1. I seriously think you need to read up more on cross-correlation functions & correlation coefficient from a statistics book, because your confusion here is more fundamental than related to MATLAB. Unless you know what you're dealing with, you

I have various time series, that I want to correlate - or rather, cross-correlate - with each other, to find out at which time lag the correlation factor is the greatest. I found various questions and answers/links discussing how to do it with numpy, but those would mean that I have to turn my dataframes into numpy arrays. And since my time series often cover different periods, I am afraid that I will run into chaos. Edit The issue I am having with all the numpy/scipy methods, is that they seem to lack awareness of the timeseries nature of my data. When I correlate a time series that starts in

I have a reference signal (s1) of length = 5 and another signal (s2) of length = 25 samples (containing a shifted version of the same 5 sample signal s1). I want to find the normalised cross correlation between the two signals to calculate the sample distance (delay / lag) between signals s1 and s2. I pad s1 with zeros (so it is the same length as s2 as required for xcorr 'coeff' option): s1(numel(s2)) = 0; and then do: [R lags]=xcorr(s2,s1,'coeff'); [vm im]=max(R); %max. correlation and index s1_lag=lags(im); to find the normalised cross-correlation for lags of -24 to 24 samples. Since s2

This is similar question to the other questions but not a duplicate one. However, I am still not able to get the correct result. I am basically trying to record two Wav files (1 - Base File 2 -Temp File) and then convert that to byte and pass to Aforge FFT and then the Correlation. There are few confusion. When I record the file I am ussing 44100 Khz with 16 bit. Therefore I believe it will return 44100 bytes per second. FFT accepts bytes in power of 2, So I am passing 16384 bytes at a time and storing that to the parent array and then I am using the Cross Corelation Alogorithm to view the

I am learning numpy/scipy, coming from a MATLAB background. The xcorr function in Matlab has an optional argument "maxlag" that limits the lag range from –maxlag to maxlag. This is very useful if you are looking at the cross-correlation between two very long time series but are only interested in the correlation within a certain time range. The performance increases are enormous considering that cross-correlation is incredibly expensive to compute. In numpy/scipy it seems there are several options for computing cross-correlation. numpy.correlate , numpy.convolve , scipy.signal.fftconvolve . If

I need to perform the autocorrelation of an array (vector) but I am having trouble finding the correct way to do so. I believe that I need the method "vDSP_conv" from the Accelerate Framework, but I can't follow how to successfully set it up. The thing throwing me off the most is the need for 2 inputs. Perhaps I have the wrong function, but I couldn't find one that operated on a single vector. The documentation can be found here Copied from the site vDSP_conv Performs either correlation or convolution on two vectors; single precision. void vDSP_conv ( const float __vDSP_signal[], vDSP_Stride _

Is there an elegant way in Matlab to get the output of normxcorr2 cropped to the size of the image or cropped only to the part of the matrix that does not use zero padded edges in computation? To understand what I mean, consider the conv2 command. There is an optional parameter called shape that can be set to same or valid . C = conv2(A,B,'same'); C = conv2(A,B,'valid'); For example: size( conv2( rand(50,50) , rand(6,6), 'valid') ) ans = 45 45 size( conv2( rand(50,50) , rand(6,6), 'same') ) ans = 50 50 size( conv2( rand(50,50) , rand(6,6)) ) ans = 55 55 Currently I wrote my own function, that