Authors: Miguel A. Molina Cabello, David A. Elizondo, Rafael Marcos Luque-Baena, Ezequiel Lopez-Rubio

Abstract: 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.

Comments: Presented at BMVC 2019: ODRSS 2019 Workshop on Object Detection and Recognition for Security Screening, Cardiff, UK.
Cite as: Paper (PDF): ODRSS2019_P_4_Cabello.pdf