Paper Info
Title
Adaptive Estimation Of Optimal Color Transformations For Deep Convolutional Network Based Homography Estimation
Abstract
Homography estimation from a pair of natural images is a problem of paramount importance for computer vision. Specialized deep convolutional neural networks have been proposed to accomplish this task. In this work, a method to enhance the result of this kind of homography estimators is proposed. Our approach generates a set of tentative color transformations for the image pair. Then the color transformed image pairs are evaluated by a regressor that estimates the quality of the homography that would be obtained by supplying the transformed image pairs to the homography estimator. Then the image pair that is predicted to yield the best result is provided to the homography estimator. Experimental results are shown, which demonstrate that our approach performs better than the direct application of the homography estimator to the original image pair, both in qualitative and quantitative terms.
Year
DOI
Venue
2020
10.1109/ICPR48806.2021.9412912
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Keywords
DocType
ISSN
homography, color shift, convolutional neural networks
Conference
1051-4651
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Miguel A. Molina-Cabello116.44
Jorge García-González262.08
Rafael Marcos Luque-Baena39613.24
Karl Thurnhofer-Hemsi436.11
Ezequiel López-Rubio500.34