Fluctuating environmental light limits number of surfaces visually recognizable by colour (original) (raw)
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Information limits on identification of natural surfaces by apparent colour
Perception, 2005
By adaptational and other mechanisms, the visual system can compensate for moderate changes in the colour of the illumination on a scene. Although the colours of most surfaces are perceived to be constant ("colour constancy"), some are not. The effect of these residual colour changes on the ability of observers to identify surfaces by their apparent colour was determined theoretically from high-resolution hyperspectral images of natural scenes under different daylights with correlated colour temperatures 4300 K, 6500 K, and 25000 K. Perceived differences between colours were estimated with an approximately uniform colour-distance measure. The information preserved under illuminant changes increased with the number of surfaces in the sample, but was limited to a relatively low asymptotic value, indicating the importance of physical factors in constraining identification by apparent colour.
Number of perceptually distinct surface colors in natural scenes
The ability to perceptually identify distinct surfaces in natural scenes by virtue of their color depends not only on the relative frequency of surface colors but also on the probabilistic nature of observer judgments. Previous methods of estimating the number of discriminable surface colors, whether based on theoretical color gamuts or recorded from real scenes, have taken a deterministic approach. Thus, a three-dimensional representation of the gamut of colors is divided into elementary cells or points which are spaced at one discrimination-threshold unit intervals and which are then counted. In this study, information-theoretic methods were used to take into account both differing surface-color frequencies and observer response uncertainty. Spectral radiances were calculated from 50 hyperspectral images of natural scenes and were represented in a perceptually almost uniform color space. The average number of perceptually distinct surface colors was estimated as 7.3 x 10^3, much smaller than that based on counting methods. This number is also much smaller than the number of distinct points in a scene that are, in principle, available for reliable identification under illuminant changes, suggesting that color constancy, or the lack of it, does not generally determine the limit on the use of color for surface identification.
Information limits on neural identification of colored surfaces in natural scenes
Visual neuroscience, 2004
If surfaces in a scene are to be distinguished by their color, their neural representation at some level should ideally vary little with the color of the illumination. Four possible neural codes were considered: von-Kries-scaled cone responses from single points in a scene, spatial ratios of cone responses produced by light reflected from pairs of points, and these quantities obtained with sharpened (opponent-cone) responses. The effectiveness of these codes in identifying surfaces was quantified by information-theoretic measures. Data were drawn from a sample of 25 rural and urban scenes imaged with a hyperspectral camera, which provided estimates of surface reflectance at 10-nm intervals at each of 1344 3 1024 pixels for each scene. In computer simulations, scenes were illuminated separately by daylights of correlated color temperatures 4000 K, 6500 K, and 25,000 K. Points were sampled randomly in each scene and identified according to each of the codes. It was found that the maximum information preserved under illuminant changes varied with the code, but for a particular code it was remarkably stable across the different scenes. The standard deviation over the 25 scenes was, on average, approximately 1 bit, suggesting that the neural coding of surface color can be optimized independent of location for any particular range of illuminants.
If surfaces from a scene are visually indistinguishable under one light, they may become distinguishable under another. The aim here was to test whether the frequency of such metamerism can be predicted by a statistical property of the colours of a scene, namely their conditional entropy. Simulations were based on 50 hyperspectral images of natural scenes under sunlight and north skylight. The correlation between the logarithm of the conditional frequency of metamerism and the conditional entropy of colours was strong, with r = 0.80–0.87. Additionally, the more likely that indistinguishable surfaces were distinguishable under a different daylight, the more reliable the prediction by conditional entropy.
Chromatic and achromatic information preserved in natural scenes under illuminant changes
The aim of this study was to determine how much information about surface colour is preserved under changes in daylight illumination. Information was quantified in the sense of Shannon and was estimated separately for chromatic and achromatic attributes of surfaces in natural scenes. Despite the large variation of luminance within natural scenes and the usually restricted gamut of colours, the information from chromatic attributes was only a little less than that from achromatic attributes. As well as providing a basis for object discrimination in natural scenes, chromatic attributes provide an important contribution towards object identification under changes in illuminant.
Reliable identification by color under natural conditions
Journal of Vision, 2009
In order to recognize objects on the basis of the way in which they reflect different wavelengths of light, the visual system must deal with the different illuminant and background conditions under which the objects are seen. To test this ability under natural conditions, subjects were asked to name 6 uniformly colored papers. The experiment started by showing subjects six papers simultaneously in a normally illuminated room, and instructing them about how to name them. The papers were easy to differentiate when seen together but they were so similar that subjects only identified 87% correctly when they were presented in isolation under otherwise identical conditions to those during the instruction. During the main part of the experiment subjects walked between several indoor and outdoor locations that differed considerably in lighting and background colors. At each location subjects were asked to identify one paper. They correctly identified the paper on 55% of the trials (well above chance level), despite the fact that the variation in the light reaching their eyes from the same paper at different positions was much larger than that from different papers at the same position. We discuss that under natural conditions color constancy is probably as good as it can be considering the theoretical limitations.
Surface chromaticity distributions of natural objects under changing illumination
2010
he problem of colour constancy is ill-posed. In order to extract surface reflectance accurately from the received colour signal, the visual system must rely on pre-imposed constraints based on properties of the natural world. Here we investigate the surface chromaticity distributions of 7 natural objects under 3 illuminations (D65, CWF and F), using a characterized Nikon D70 SLR camera. We find that these object surfaces exhibit intrinsic chromatic textures and provide a large number of reflectance samples on their own. The information may thereby be utilized to improve colour constancy over that achievable with artificial surfaces possessing single or limited chromaticities. By analyzing the pattern of the chromaticity distributions under changing illumination, we find that the distributions of within-surface cone contrasts for given objects form distinct signatures in cone-contrast space. These signatures transform predictably under changes in illumination. We suggest that this feature may be utilized to aid colour constancy.
How daylight influences high-order chromatic descriptors in natural images
Applied Optics, 2017
Despite the global and local daylight changes naturally occurring in natural scenes, the human visual system usually adapts quite well to those changes, developing a stable color perception. Nevertheless, the influence of daylight in modeling natural image statistics is not fully understood and has received little attention. The aim of this work was to analyze the influence of daylight changes in different high-order chromatic descriptors (i.e., color volume, color gamut, and number of discernible colors) derived from 350 color images, which were rendered under 108 natural illuminants with Correlated Color Temperatures (CCT) from 2735 to 25,889 K. Results suggest that chromatic and luminance information is almost constant and does not depend on the CCT of the illuminant for values above 14,000 K. Nevertheless, differences between the red-green and blue-yellow image components were found below that CCT, with most of the statistical descriptors analyzed showing local extremes in the range 2950 K-6300 K. Uniform regions and areas of the images attracting observers' attention were also considered in this analysis and were characterized by their patchiness index and their saliency maps. Meanwhile, the results of the patchiness index do not show a clear dependence on CCT, and it is remarkable that a significant reduction in the number of discernible colors (58% on average) was found when the images were masked with their corresponding saliency maps. Our results suggest that chromatic diversity, as defined in terms of the discernible colors, can be strongly reduced when an observer scans a natural scene. These findings support the idea that a reduction in the number of discernible colors will guide visual saliency and attention. Whatever the modeling is mediating the neural representation of natural images, natural image statistics, it is clear that natural image statistics should take into account those local maxima and minima depending on the daylight illumination and the reduction of the number of discernible colors when salient regions are considered.
Discrimination of spectral reflectance under environmental illumination
Journal of the Optical Society of America, 2018
Color constancy is the ability to recover a stable perceptual estimate of surface reflectance, regardless of the lighting environment. However, we know little about how observers make judgments of the surface color of glossy objects, particularly in complex lighting environments that introduce complex spatial patterns of chromatic variation across an object's surface. To address this question, we measured thresholds for reflectance discrimination using computer-rendered stimuli under environmental illumination. In Experiment 1, we found that glossiness and shape had small effects on discrimination thresholds. Importantly, discrimination ellipses extended along the direction in which the chromaticities in the environmental illumination spread. In Experiment 2, we also found that the observers' abilities to judge surface colors were worse in lighting environments with an atypical chromatic distribution.