Name

ccfn - calculate the normalized circulant cross-correlation function between two images using multiplication in Fourier space.

Usage

output = ccfn(image, ref, center=True)

Input

image
input image (real)
ref
second input image (real) (in the alignment problems, it should be the reference image).
center
if set to True (default), the origin of the result is at the center; if set to False, the origin is at (0,0), the option is much faster, but the result is difficult to use

Output

output
normalized circulant cross-correlation function between image and ref. Real. The origin of the cross-correlation function (term ccf(0,0,0)) is located at (int[n/2], int[n/2], int[n/2]) in 3D, (int[n/2], int[n/2]) in 2D, and at int[n/2] in 1D.

Method

first normalization of both images by subtracting their respective averages and by dividing them by their respective

transform is calculated to yield ccfn.

from the origin. More distant ccfn values are corrupted by the "wrap around" artifacts.

Reference

Pratt, W. K., 1992. Digital image processing. Wiley, New York.

Author / Maintainer

Pawel A. Penczek

Keywords

category 1
FUNDAMENTALS
category 2
FOURIER

Files

fundamentals.py

Maturity

stable
works for most people, has been tested; test cases/examples available.

Bugs

None. It is perfect.

ccfn (last edited 2013-07-01 13:13:02 by localhost)