- What is normalized cross-correlation?
- What is lag in cross-correlation?
- What does cross-correlation tell us?
- What does negative cross-correlation mean?
What is normalized cross-correlation?
Description. Normalized Cross-Correlation (NCC) is by definition the inverse Fourier transform of the convolution of the Fourier transform of two (in this case) images, normalized using the local sums and sigmas (see below).
What is lag in cross-correlation?
The lag refers to how far the series are offset, and its sign determines which series is shifted. Note that as the lag increases, the number of possible matches decreases because the series “hang out” at the ends and do not overlap.
What does cross-correlation tell us?
Cross-correlation is a measurement that tracks the movements of two or more sets of time series data relative to one another. It is used to compare multiple time series and objectively determine how well they match up with each other and, in particular, at what point the best match occurs.
What does negative cross-correlation mean?
A negative correlation describes the extent to which two variables move in opposite directions. For example, for two variables, X and Y, an increase in X is associated with a decrease in Y. A negative correlation coefficient is also referred to as an inverse correlation.