@Article{ArjanGijsenji2011,
author="Arjan Gijsenji
and Theo Gevers
and Joost Van de Weijer",
title="Computational Color Constancy: Survey and Experiments",
journal="IEEE Transactions on Image Processing",
year="2011",
volume="20",
number="9",
pages="2475--2489",
optkeywords="computational color constancy",
optkeywords="computer vision application",
optkeywords="gamut-based method",
optkeywords="learning-based method",
optkeywords="static method",
optkeywords="colour vision",
optkeywords="computer vision",
optkeywords="image colour analysis",
optkeywords="learning (artificial intelligence)",
optkeywords="lighting",
abstract="Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets.",
optnote="ISE;CIC",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1717), last updated on Fri, 04 Feb 2022 12:57:40 +0100",
issn="1057-7149",
doi="10.1109/TIP.2011.2118224",
opturl="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5719167",
file=":http://refbase.cvc.uab.es/files/GGW2011.pdf:PDF"
}