Finding color representations that are stable to illuminant changes is still an open problem in
computer vision. Until now, most approaches have been based on physical constraints or …

Computational color constancy aims to estimate the actual color in an acquired scene
disregarding its illuminant. Many illuminant estimation solutions have been proposed in the …

The effects of strong color casts in traditional, learning-based and data-driven color
constancy algorithms is analyzed. This is the first study investigating the response of color …

In many tasks of machine vision applications, it is important that recorded colors remain
constant, in the real world scene, even under changes of the illuminants and the cameras …

We introduce an efficient maximum likelihood approach for one part of the color constancy
problem: removing from an image the color cast caused by the spectral distribution of the …

The color of a scene recorded by a trichromatic sensor varies with the spectral distribution of
the illuminant. For recognition and many other applications, we seek to process these …

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 …

The problem of removing illuminant variations to preserve the colours of objects (colour
constancy) has already been solved by the human brain using mechanisms that rely largely …

The paper considers the problem of illuminant estimation: how, given an image of a scene,
recorded under an unknown light, we can recover an estimate of that light. Obtaining such …

A neural network can learn color constancy, defined here as the ability to estimate the
chromaticity of a scene's overall illumination. We describe a multilayer neural network that is …