Abstract not found

Abstract Light, which is reflected from an object, varies with the type of illuminant used. Nevertheless, the color of an object appears to be approximately constant to a human observer. The ability to compute color constant descriptors from reflected light, is called color constancy. In order to solve the problem of color constancy, some assumptions have to be made. One frequently made assumption is that on average, the world is gray. We address the problem of color constancy and focus on the use of space average color for color constancy. Instead of computing global space average color we suggest to use local space average color as the illuminant frequently varies across an image. We discuss several different methods on how to compute local space average color. The performance of the different algorithms as well as related algorithms is evaluated on an object recognition task. Algorithms based on local space average color are simple, yet highly effective for the problem of color constancy. Such algorithms are particularly suited for object recognition tasks.

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-theart 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. Index Terms—color constancy, illuminant estimation, survey, performance evaluation.

The present work demonstrates for the first time the advantage of an analytical Gaussian model of spectral function approximation for solving the colour constancy problem. This model was compared with well-known linear models in numerical simulations performed over an extensive set of natural pigments illuminated by both ideal Planck and real light sources. The precision and stability of the colour constancy were estimated using the same colour constancy algorithm for all the models compared. The experiments indicate that the Gaussian model is potentially more effective for estimating the chromaticity of the scene illuminant and the scene objects than the linear spectral models are. In addition, the Gaussian model involves the possibility of comparing the object colour with the etalon colour captured by another sensor.

We present image segmentation and highlight detection algorithms based on the dichromatic reflection model. For image segmentation, we use the model prediction that objects of a certain colour produce lines (the matte lines) radiating away from the origin of the RGB colour space. These

Performance measures for quantifying human color constancy and computational color constancy are very different. The former relate to measurements on individual object colors whereas the latter relate to the accuracy of the estimated illuminant. To bridge this gap, we propose a psychophysical method in which observers judge the global color fidelity of the visual scene rendered under different illuminants. In each experimental trial, the scene is rendered under three illuminants, two chromatic test illuminants and one neutral reference illuminant. Observers indicate which of the two test illuminants leads to better color fidelity in comparison to the reference illuminant. Here we study multicolor scenes with chromatic distributions that are differently oriented in color space, while having the same average chromaticity. We show that when these distributions are rendered under colored illumination they lead to different perceptual estimates of the color fidelity. Index Terms — Color constancy, color fidelity, triad comparison, chromatic distributions

The White-Patch method, one of the very first colour constancy methods, estimates the illuminant colour from the maximum response of three colour channels. However, this simple method has been superseded by advanced physical, statistical and learning based colour constancy methods. Recently, a few research works have suggested that the simple idea of using maximum pixel values is not as limited an idea as it seems on first glance. These works show that in several situations some manipulations can indeed made it perform very well. Here, we extend the White-Patch assumption to include any of: white patch, highlights or light source; let us refer to these pixels in an image as the “bright ” pixels areas. We propose that bright pixels are surprisingly helpful in the illumination estimation process. In this paper, we investigate the effects of bright pixels on several current colour constancy algorithms. Moreover, we describe a simple framework for an illumination estimation method based on bright pixels and compare its accuracy to well-known colour constancy algorithms applied to four standard datasets. We also investigate failure and success cases, using bright pixels, and propose desiderata on input images with regard to the proposed method.