  To make Robotics and Augmented Reality applications robust to illumination
changes, the current trend is to train a Deep Network with training images
captured under many different lighting conditions. Unfortunately, creating such
a training set is a very unwieldy and complex task. We therefore propose a
novel illumination normalization method that can easily be used for different
problems with challenging illumination conditions. Our preliminary experiments
show that among current normalization methods, the Difference-of Gaussians
method remains a very good baseline, and we introduce a novel illumination
normalization model that generalizes it. Our key insight is then that the
normalization parameters should depend on the input image, and we aim to train
a Convolutional Neural Network to predict these parameters from the input
image. This, however, cannot be done in a supervised manner, as the optimal
parameters are not known a priori. We thus designed a method to train this
network jointly with another network that aims to recognize objects under
different illuminations: The latter network performs well when the former
network predicts good values for the normalization parameters. We show that our
method significantly outperforms standard normalization methods and would also
be appear to be universal since it does not have to be re-trained for each new
application. Our method improves the robustness to light changes of
state-of-the-art 3D object detection and face recognition methods.
