A multi-cue illumination estimation method based on tree-structured group joint sparse representation is proposed. Tests show that the proposed method works better than existing methods, most of which are based on using only a single cue type, for example, a binarized color histogram or simple image statistic such as the mean RGB. Most existing illumination estimation methods make their estimates using only one of three kinds of cues. They differ in which cue type they use, but the chosen cue is either based on (1) properties of the low-level RGB color distribution, (2) mid-level initial illuminant estimates provided by subordinate methods, or (3) high-level knowledge of scene content (e.g., indoor versus outdoor scene). The proposed multi-cue method combines the information provided by cues of all three of these types within the framework of a tree-structured group joint sparse representation (TGJSR). In TGJSR, the training data is grouped into a tree of subgroups. A test image under an unknown illuminant has its features reconstructed in terms of a joint sparse representation model derived from the grouped training data. The test image's illumination is then estimated based on the weights involved in the joint sparse representation model. As a general framework, the proposed TGJSR framework can also easily be extended to incorporate any new features or cues that might be discovered in the future for illumination estimation.

Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation

A limitation in color constancy research is the inability to establish ground truth colors for evaluating corrected images. Many existing datasets contain images of scenes with a color chart included, however, only the chart's neutral colors (grayscale patches) are used to provide the ground truth for illumination estimation and correction. This is because the corrected neutral colors are known to lie along the achromatic line in the camera's color space (i.e. R=G=B), the correct RGB values of the other color patches are not known. As a result, most methods estimate a 3*3 diagonal matrix that ensures only the neutral colors are correct. In this paper, we describe how to overcome this limitation. Specifically, we show that under certain illuminations, a diagonal 3*3 matrix is capable of correcting not only neutral colors, but all the colors in a scene. This finding allows us to find the ground truth RGB values for the color chart in the camera's color space. We show how to use this information to correct all the images in existing datasets to have correct colors. Working from these new color corrected datasets, we describe how to modify existing color constancy algorithms to perform better image correction.

/pdf/beyond-white-ground-truth-colors-for-color-constancy-5d0rbgvcz4.pdf

Beyond White: Ground Truth Colors for Color Constancy Correction

The origin of colour categories and their relationship to colour perception have been the prime example for testing the influence of language on perception and thought (Sapir-Whorf hypothesis) and more generally for investigating the biological, ecological and cultural determination of human cognition (nature-nurture debate). These themes are central to a broad range of disciplines, including vision research, neuroscience, cognitive psychology, developmental science, cultural anthropology, linguistics, computer science, and philosophy. Unfortunately, though, it has been tacitly taken for granted that the conceptual assumptions and methodological practices from the dawn of empirical research on colour categorisation are the gold standard for current colour category research. Here, we show that these assumptions and practices are obsolete and flawed and have led to four fundamental and widespread misconceptions about colour categorisation: 1.) that colour perception is inherently categorical; 2.) that English Basic Colour Terms correspond to universal categories that are the end point of a fixed evolutionary sequence; 3.) that the prototypes of English basic colour terms are perceptually salient and qualify as focal colours; and 4.) that colour category research essentially revolves around the universalism-realism debate. State-of-the-art research on colour categorisation provides new, more sophisticated approaches and allows for rectifying those four statements. At the same time, some of the questions underlying those statements are not convincingly answered yet and constitute major challenges to future research. The critical considerations on colour categorisation may be transferred to research on other kinds of perceptual categorisation to inspire new, more general research questions.

/pdf/misconceptions-about-colour-categories-137womihxp.pdf

Misconceptions About Colour Categories

A new algorithm for calculating the metamer mismatch volumes that arise in colour vision and colour imaging is introduced. Unlike previous methods, the proposed method places no restrictions on the set of possible object reflectance spectra. As a result of such restrictions, previous methods have only been able to provide approximate solutions to the mismatch volume. The proposed new method is the first to characterize precisely the metamer mismatch volume for any possible reflectance.

http://ieeexplore.ieee.org/iel7/83/6657713/06606818.pdf

Metamer Mismatching

We investigated several sensory and cognitive determinants of colour constancy across 40 illumination hues. In the first experiment, we measured colour naming for the illumination and for the colour induced by the illumination on the colorimetric grey. Results confirmed that the induced colours are approximately complementary to the colour of the illumination. In the second experiment, we measured colour constancy using achromatic adjustments. Average colour constancy was perfect under the blue daylight illumination and decreased in colour directions away from the blue daylight illumination due to undershooting and a strong blue bias. Apart from this blue bias, colour constancy was not related to illumination discrimination and to chromatic detection measured previously with the same setup and stimuli. We also observed a strong negative relationship between the degree of colour constancy and the consensus of naming the illumination colour. Constancy coincided with a low naming consensus, in particular because bluish illumination colours were sometimes seen as achromatic. Blue bias and category consensus alone explained >68%, and all determinants together explained >94% of the variance of achromatic adjustments. These findings suggest that colour constancy is optimised for blue daylight.

https://journals.sagepub.com/doi/pdf/10.1177/2041669517739635

Determinants of Colour Constancy and the Blue Bias

Color constancy is the ability to recognize the color of an object (or more generally of a surface) under different illuminations. Without color constancy, surface color as a perceptual attribute would not be meaningful in the visual environment, where illumination changes all the time. Nevertheless, it is not obvious how color constancy is possible in the light of metamer mismatching. Surfaces that produce exactly the same sensory color signal under one illumination (metamerism) may produce utterly different sensory signals under another illumination (metamer mismatching). Here we show that this phenomenon explains to a large extent the variation of color constancy across different colors. For this purpose, color constancy was measured for different colors in an asymmetric matching task with photorealistic images. Color constancy performance was strongly correlated to the size of metamer mismatch volumes, which describe the uncertainty of the sensory signal due to metamer mismatching for a given color. The higher the uncertainty of the sensory signal, the lower the observers' color constancy. At the same time, sensory singularities, color categories, and cone ratios did not affect color constancy. The present findings do not only provide considerable insight into the determinants of color constancy, they also show that metamer mismatch volumes must be taken into account when investigating color as a perceptual property of objects and surfaces.

Uncertainty of sensory signal explains variation of color constancy.

Recently, Logvinenko introduced a new object-color space defining a complete object-color atlas that is invariant to illumination.1 However, the existing implementation for calculating the new atlas's color descriptors is computationally expensive and does not work for all types of illuminants. A new algorithm is presented here that efficiently calculates the required color descriptors over large data sets and across a wide variety of illuminants. Its Matlab implementation has been made available online. The algorithm is then used to explore some features and possible applications of Logvinenko's color atlas. In particular, it is applied to images to investigate the perceptual correlates of the color descriptors; it is used to predict how images change under a change of scene illumination; and it is used to evaluate how changes in illumination and sensor sensitivities affect the mapping from the Munsell to NCS color atlases. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011;

The Logvinenko object color atlas in practice

Recently Logvinenko introduced a new object- color space, establishing a complete color atlas that is invariant to illumination (2). However, the existing implementation for calcu- lating the proposed color descriptors is computationally expensive and does not work for all types of illuminants. A new algorithm is presented that allows for an efficient calculation of Logvinenko's color descriptors for large data sets and a wide variety of illumi- nants.

/pdf/xyz-to-adl-calculating-logvinenko-s-object-color-coordinates-55o7oz9wk4.pdf

Christoph Godau

Papers

Metamer Mismatching

Uncertainty of sensory signal explains variation of color constancy.

The Logvinenko object color atlas in practice

XYZ to ADL: Calculating Logvinenko's Object Color Coordinates

How metamer mismatching decreases as the number of colour mechanisms increases with implications for colour and lightness constancy