Color constancy based on the Grey-edge hypothesis

International audienceA well-known color constancy method is based on the Grey-World assumption i.e. 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 assuming that the average edge difference in a scene is achromatic. Based on this hypothesis, we propose an algorithm for color constancy. Recently, the Grey-World hypothesis and the max-RGB method were shown to be two instantiations of a Minkowski norm based color constancy method. Similarly we also propose a more general version of the Grey-Edge hypothesis which assumes that the Minkowsky norm of derivatives of the reflectance of surfaces is achromatic. The algorithms are tested on a large data set of images under different illuminants, and the results show that the new method outperforms the Grey-World assumption and the max-RGB method. Results are comparable to more elaborate algorithms, however at lower computational costs

Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism

Pooling is a ubiquitous operation in image processing algorithms that allows for higher-level processes to collect relevant low-level features from a region of interest. Currently, max-pooling is one of the most commonly used operators in the computational literature. However, it can lack robustness to outliers due to the fact that it relies merely on the peak of a function. Pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. The receptive fields of these neurons have been shown to vary according to the contrast by aggregating signals over a larger region in the presence of low contrast stimuli. We hypothesise that this contrast-variant-pooling mechanism can address some of the shortcomings of max-pooling. We modelled this contrast variation through a histogram clipping in which the percentage of pooled signal is inversely proportional to the local contrast of an image. We tested our hypothesis by applying it to the phenomenon of colour constancy where a number of popular algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and Double-Opponency). For each of these methods, we investigated the consequences of replacing their original max-pooling by the proposed contrast-variant-pooling. Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism

The effects of belongingness on the Simultaneous Lightness Contrast: A virtual reality study

Simultaneous Lightness Contrast (SLC) is the phenomenon whereby a grey patch on a dark background appears lighter than an equal patch on a light background. Interestingly, the lightness difference between these patches undergoes substantial augmentation when the two backgrounds are patterned, thereby forming the articulated-SLC display. There are two main interpretations of these phenomena: The midlevel interpretation maintains that the visual system groups the luminance within a set of contiguous frameworks, whilst the high-level one claims that the visual system splits the luminance into separate overlapping layers corresponding to separate physical contributions. This research aimed to test these two interpretations by systematically manipulating the viewing distance and the horizontal distance between the backgrounds of both the articulated and plain SLC displays. An immersive 3D Virtual Reality system was employed to reproduce identical alignment and distances, as well as isolating participants from interfering luminance. Results showed that reducing the viewing distance resulted in increased contrast in both the plain- and articulated-SLC displays and that, increasing the horizontal distance between the backgrounds resulted in decreased contrast in the articulated condition but increased contrast in the plain condition. These results suggest that a comprehensive lightness theory should combine the two interpretations

Kirschmann's Fourth Law

Kirschmann's Fourth Law states that the magnitude of simultaneous color contrast increases with the saturation of the inducing surround, but that the rate of increase reduces as saturation increases. Others since Kirschmann have agreed and disagreed. Here we show that the form of the relationship between simultaneous color contrast and inducer saturation depends on the method of measurement. Functions were measured by four methods: (i) asymmetric matching with a black surround, (ii) asymmetric matching with a surround metameric to equal energy white, (iii) dichoptic matching, and (iv) nulling an induced sinusoidal modulation. Results from the asymmetric matching conditions agreed with Kirschmann, whereas results from nulling and from dichoptic matching showed a more linear increase in simultaneous contrast with the saturation of the inducer. We conclude that the method certainly affects the conclusions reached, and that there may not be any "fair" way of measuring simultaneous contrast

The reproduction angular error for evaluating the performance of illuminant estimation algorithms

The angle between the RGBs of the measured illuminant and estimated illuminant colors - the recovery angular error - has been used to evaluate the performance of the illuminant estimation algorithms. However we noticed that this metric is not in line with how the illuminant estimates are used. Normally, the illuminant estimates are ‘divided out’ from the image to, hopefully, provide image colors that are not confounded by the color of the light. However, even though the same reproduction results the same scene might have a large range of recovery errors. In this work the scale of the problem with the recovery error is quantified. Next we propose a new metric for evaluating illuminant estimation algorithms, called the reproduction angular error, which is defined as the angle between the RGB of a white surface when the actual and estimated illuminations are ‘divided out’. Our new metric ties algorithm performance to how the illuminant estimates are used. For a given algorithm, adopting the new reproduction angular error leads to different optimal parameters. Further the ranked list of best to worst algorithms changes when the reproduction angular is used. The importance of using an appropriate performance metric is established

A Psychophysical Analysis of Illuminant Estimation Algorithms

Illuminant estimation algorithms are often evaluated by calculating recovery angular error which is the angle between the RGB of the ground truth and the estimated illuminants. However, the same scene viewed under two different lights with respect to which the same algorithm delivers illuminant estimates and then identical reproductions - and so, the practical estimation error is the same - can, in fact and counterintuitively, result in quite different recovery errors. Reproduction angular error has been recently introduced as an improvement to recovery angular error. The new metric calculates the angle between the RGB values of a white surface corrected by the ground truth illuminant and corrected by the estimated illuminant. Experiments show that illuminant estimation algorithms could be ranked differently depending on whether they are evaluated by recovery or reproduction angular error. In this paper a psychophysical experiment is designed which demonstrates that observers choices on 'what makes a good reproduction' correlates with reproduction error and not recovery error

Colour Relations in Form

The orthodox monadic determination thesis holds that we represent colour relations by virtue of representing colours. Against this orthodoxy, I argue that it is possible to represent colour relations without representing any colours. I present a model of iconic perceptual content that allows for such primitive relational colour representation, and provide four empirical arguments in its support. I close by surveying alternative views of the relationship between monadic and relational colour representation

True colour retrieval from multiple illuminant scene’s image

This paper presents an algorithm to retrieve the true colour of an image captured under multiple illuminant. The proposed method uses a histogram analysis and K-means++ clustering technique to split the input image into a number of segments. It then determines normalised average absolute difference (NAAD) for each resulting segment’s colour component. If the NAAD of the segment’s component is greater than an empirically determined threshold. It assumes that the segment does not represent a uniform colour area, hence the segment’s colour component is selected to be used for image colour constancy adjustment. The initial colour balancing factor for each chosen segment’s component is calculated using the Minkowski norm based on the principal that the average values of image colour components are achromatic. It finally calculates colour constancy adjustment factors for each image pixel by fusing the initial colour constancy factors of the chosen segments weighted by the normalised Euclidian distances of the pixel from the centroids of the selected segments. Experimental results using benchmark single and multiple illuminant image datasets, show that the proposed method’s images subjectively exhibit highest colour constancy in the presence of multiple illuminant and also when image contains uniform colour areas