Editing Image registration (section)

=== Spatial vs frequency domain methods ===

Spatial methods operate in the image domain, matching intensity patterns or features in images. Some of the feature matching algorithms are outgrowths of traditional techniques for performing manual image registration, in which an operator chooses corresponding [[Feature (computer vision)|control points]] (CP) in images. When the number of control points exceeds the minimum required to define the appropriate transformation model, iterative algorithms like [[RANSAC]] can be used to robustly estimate the parameters of a particular transformation type (e.g. affine) for registration of the images.

Frequency-domain methods find the transformation parameters for registration of the images while working in the transform domain. Such methods work for simple transformations, such as translation, rotation, and scaling. Applying the [[phase correlation]] method to a pair of images produces a third image which contains a single peak. The location of this peak corresponds to the relative translation between the images. Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects typical of medical or satellite images. Additionally, the phase correlation uses the [[fast Fourier transform]] to compute the cross-correlation between the two images, generally resulting in large performance gains. The method can be extended to determine rotation and scaling differences between two images by first converting the images to [[log-polar coordinates]].<ref>{{cite journal|author=B. Srinivasa Reddy|author2=B. N. Chatterji|title=An FFT-Based Technique for Translation, Rotation and Scale-Invariant Image Registration|journal=IEEE Transactions on Image Processing|volume=5|issue=8|pages=1266–1271|date=Aug 1996|doi=10.1109/83.506761|pmid=18285214|bibcode=1996ITIP....5.1266R |s2cid=6562358}}</ref><ref>Zokai, S., Wolberg, G., [https://www-cs.ccny.cuny.edu/~wolberg/pub/tip05.pdf "Image Registration Using Log-Polar Mappings for Recovery of Large-Scale Similarity and Projective Transformations"]. ''IEEE Transactions on Image Processing'', vol. 14, No. 10, October, 2005.</ref> Due to properties of the [[Fourier transform]], the rotation and scaling parameters can be determined in a manner invariant to translation.