cross-correlation function

"Cross-correlation function" is a function expressing to what extent arbitrary two functions are similar or to what extent the two functions are shifted. It is a function (or a pattern) that is acquired by integrating the product (overlap) of arbitrary two functions with respect to a variable which are shifted by a certain amount about the variable. That is, when we define the object functions f, g, an integral variable of the functions X and a relative shift of the two functions x, the cross-correlation function R_fg can be written as the following equation: R_fg=∫f(X)g*(X-x)dX. Note that * denotes complex conjugate. In the case of a microscope image, etc., the functions f and g are real, and then g*(X-x)=g(X-x). If the two object functions are the same, the cross-correlation function is reduced to the auto-correlation function. If the value of R_fg is large, it indicates that the two functions (or patterns) are similar to each other. If R_fg takes a large value for a certain value of x, the relative shift between the two functions is obtained. For example, when the cross-correlation function is calculated for two TEM images taken successively, it gives the knowledge about the image drift during the acquisition of the images. (If the value of the correlation function is large for a small value of x, this indicates a small image drift.) For high-speed computer calculation of the cross-correlation function, FFT (fast Fourier transform) is used on the basis of the following theorem: Fourier transform of the product of certain functions is equivalent to the product of Fourier transforms of the respective functions. That is, the cross-correlation function is calculated by the inverse Fourier transform of the power of the Fourier transforms of the respective functions.

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