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Scikit (2021) Using Polar and Log-Polar Transformations for Registration.
https://scikit-image.org/docs/stable/auto_examples/registration/plot_register_rotation.html

has been cited by the following article:

• TITLE: Evidence of the Great Attractor and Great Repeller from Artificial Neural Network Imputation of Sloan Digital Sky Survey

  AUTHORS: Christopher Cillian O’Neill

  KEYWORDS: Artificial Neural Networks, Convolutional Neural Networks, SDSS, Anisotropies, Great Attractor

  JOURNAL NAME: Journal of High Energy Physics, Gravitation and Cosmology, Vol.10 No.3, July 31, 2024

  ABSTRACT: The Sloane Digital Sky Survey (SDSS) has been in the process of creating a 3D digital map of the Universe, since 2000AD. However, it has not been able to map that portion of the sky which is occluded by the dust gas and stars of our own Milkyway Galaxy. This research builds on work from a previous paper that sought to impute this missing galactic information using Inpainting, polar transforms and Linear Regression ANNs. In that paper, the author only attempted to impute the data in the Northern hemisphere using the ANN model, which subsequently confirmed the existence of the Great Attractor and the homogeneity of the Universe. In this paper, the author has imputed the Southern Hemisphere and discovered a region that is mostly devoid of stars. Since this area appears to be the counterpart to the Great Attractor, the author refers to it as the Great Repeller and postulates that it is an area of physical repulsion, inline with the work of GerdPommerenke and others. Finally, the paper investigates large scale structures in the imputed galaxies.