Color constancy is the ability to measure colors of objects independent of the color of the light source. A well-known color constancy method is based on the Grey-World assumption which assumes that the average reflectance of surfaces in the world is achromatic. In this article, we propose a new hypothesis for color constancy namely the Grey-Edge hypothesis, which assumes that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Contrary to existing color constancy algorithms, which are computed from the zero-order structure of images, our method is based on the derivative structure of images. Furthermore, we propose a framework which unifies a variety of known (Grey-World, max-RGB, Minkowski norm) and the newly proposed Grey-Edge and higher-order Grey-Edge algorithms. The quality of the various instantiations of the framework is tested and compared to the state-of-the-art color constancy methods on two large data sets of images recording objects under a large number of different light sources. The experiments show that the proposed color constancy algorithms obtain comparable results as the state-of-the-art color constancy methods with the merit of being computationally more efficient.

Estimating the color of a scene illuminant often plays a central role in computational color constancy. While this problem has received significant attention, the methods that exist do not maximally leverage spatial dependencies between pixels. Indeed, most methods treat the observed color (or its spatial derivative) at each pixel independently of its neighbors. We propose an alternative approach to illuminant estimation—one that employs an explicit statistical model to capture the spatial dependencies between pixels induced by the surfaces they observe. The parameters of this model are estimated from a training set of natural images captured under canonical illumination, and for a new image, an appropriate transform is found such that the corrected

In this paper, we propose a new algorithm for illuminant estimation. We begin by reviewing the concept of chromagenic colour constancy, where two pictures are taken from each scene: a normal one and one where a coloured filter is placed in front of the camera, and look at parameters known to affect its performance such as filters and sensor choice. We show that the basic formulation of the chromagenic algorithm has inherent weaknesses: a need for perfectly registered images and occasional large errors in illuminant estimation. Our first contribution is to analyse the algorithm performance with respect to the reflectances present in a scene and demonstrate that fairly bright and desaturated reflectances (e.g., achromatic and pastel colours) provide significantly better chromagenic illuminant estimation. This analysis leads to the bright-chromagenic algorithm. We show that it not only remedies the large error problem but also allows us to relax the image registration constraint. Experiments performed on a variety of synthetic and real data show that the newly designed brightchromagenic algorithm significantly-in a strict statistical sense- outperforms current illuminant estimation methods, including those having a substantially higher complexity.

Color constancy is one of the important research areas with a wide range of applications in the fields of color image processing and computer vision. One such application is video tracking. Color is used as one of the salient features and its robustness to illumination variation is essential to the adaptability of video tracking algorithms. Color constancy can be applied to discount the influence of changing illuminations. In this paper, we present a review of established color constancy approaches. We also investigate whether these approaches in their present form of implementation can be applied to the video tracking problem. The approaches are grouped into two categories, namely, Pre-Calibrated and Data-driven approaches. The paper also talks about the ill-posedness of the color constancy problem, implementation assumptions of color constancy approaches, and problem statement for tracking. Publications on video tracking algorithms involving color correction or color compensation techniques are not included in this review.

We propose a simple nonparametric linear regression tool, known as kernel regression (KR), to estimate the illumination chromaticity. We design a Gaussian kernel whose bandwidth is selected empirically. Previously, nonlinear techniques like neural networks (NN) and support vector machines (SVM) are applied to estimate the illumination chromaticity. However, neither of the techniques was compared with linear regression tools. We show that the proposed method performs better chromaticity estimation compared to NN, SVM, and linear ridge regression (RR) approach on the same data set. Index Terms — Kernel regression, Color constancy 1.

In this paper, we present the application of two linear machine learning techniques; ridge regression and kernel regression for the estimation of illumination chromaticity. A number of machine learning techniques, neural networks and support vector machines in particular, are used to estimate the illumination chromaticity. These nonlinear approaches are shown to outperform many traditional algorithms. However, neither neural networks nor support vector machines were compared to linear regression tools in the past. We evaluate the performance of linear machine learning techniques and draw comparison with nonlinear machine learning techniques. Kernel regression achieves a mean root mean square chromaticity error of 0.052 while neural network results in 0.071. An improvement of 26% is achieved. Both quantitative and qualitative results show that the performances of the linear techniques are better when compared to nonlinear techniques on the same data set. Machine learning approaches are also compared with the gray-world and the scale by max algorithms. We perform uncertainty analysis of machine learning algorithms using a bootstrapped training data set to evaluate their consistency in the estimation of illumination chromaticity. Applications like video tracking and target detection, where illumination chromaticity estimation is important will be benefited by a better performance of linear machine learning algorithms.

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.

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