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Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow
Oliver Demetz1, Michael Stoll2, Sebastian Volz2, Joachim Weickert1, and Andrés Bruhn2
1Mathematical Analysis Group, Saarland University, Saarbrücken, Germany
demetz@mia.uni-saarland.de
weickert@mia.uni-saarland.de
2Institute for Visualization and Interactive Systems, University of Stuttgart, Germany
stoll@vis.uni-stuttgart.de
volz@vis.uni-stuttgart.de
bruhn@vis.uni-stuttgart.de
Abstract. The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation.
LNCS 8689, p. 455 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014