@Article{ShidaBeigpour2014,
author="Shida Beigpour
and Christian Riess
and Joost Van de Weijer
and Elli Angelopoulou",
title="Multi-Illuminant Estimation with Conditional Random Fields",
journal="IEEE Transactions on Image Processing",
year="2014",
volume="23",
number="1",
pages="83--95",
optkeywords="color constancy",
optkeywords="CRF",
optkeywords="multi-illuminant",
abstract="Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.",
optnote="CIC; LAMP; 600.074; 600.079",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2451), last updated on Fri, 04 Feb 2022 13:11:14 +0100",
issn="1057-7149",
doi="10.1109/TIP.2013.2286327",
file=":http://refbase.cvc.uab.es/files/BRW2014.pdf:PDF"
}