Homography estimation with deep convolutional neural networks by random color transformations
Miguel A. Molina‐Cabello, David A. Elizondo, Rafael Marcos Luque Baena, Ezequiel López‐Rubio · Repositorio Institucional de la Universidad de Málaga (University of Málaga) · 2019
Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.