Covariance of 2D cross correlation result

1. How do you calculate 2D cross-correlation?
2. What is the relation between covariance and cross-correlation?
3. How do you interpret cross-correlation results?
4. How do you interpret cross-covariance?

How do you calculate 2D cross-correlation?

c = xcorr2( a , b ) returns the cross-correlation of matrices a and b with no scaling. xcorr2 is the two-dimensional version of xcorr . c = xcorr2( a ) is the autocorrelation matrix of input matrix a . This syntax is equivalent to xcorr2(a,a) .

What is the relation between covariance and cross-correlation?

Cross-covariance is related to the more commonly used cross-correlation of the processes in question. itself. In signal processing, the cross-covariance is often called cross-correlation and is a measure of similarity of two signals, commonly used to find features in an unknown signal by comparing it to a known one.

How do you interpret cross-correlation results?

If the slope is positive, the cross correlation is positive; if there is a negative slope, the cross correlation is negative. This helps to identify important lags (or leads) in the process and is useful for application when there are predictors in an ARIMA model.

How do you interpret cross-covariance?

If cov(X, Y) is positive, then larger values of X are associated with larger values of Y and smaller values of X are associated with smaller values of Y. If the covariance is negative, the opposite holds: small Xs are associated with larger Ys and vice versa.