Chromatic Illumination Discrimination Ability Reveals that Human Colour Constancy Is Optimised for Blue Daylight Illuminations

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed

Estimation of illuminants from color signals of illuminated objects

Color constancy is the ability of the human visual systems to discount the effect of the illumination and to assign approximate constant color descriptions to objects. This ability has long been studied and widely applied to many areas such as color reproduction and machine vision, especially with the development of digital color processing. This thesis work makes some improvements in illuminant estimation and computational color constancy based on the study and testing of existing algorithms. During recent years, it has been noticed that illuminant estimation based on gamut comparison is efficient and simple to implement. Although numerous investigations have been done in this field, there are still some deficiencies. A large part of this thesis has been work in the area of illuminant estimation through gamut comparison. Noting the importance of color lightness in gamut comparison, and also in order to simplify three-dimensional gamut calculation, a new illuminant estimation method is proposed through gamut comparison at separated lightness levels. Maximum color separation is a color constancy method which is based on the assumption that colors in a scene will obtain the largest gamut area under white illumination. The method was further derived and improved in this thesis to make it applicable and efficient. In addition, some intrinsic questions in gamut comparison methods, for example the relationship between the color space and the application of gamut or probability distribution, were investigated. Color constancy methods through spectral recovery have the limitation that there is no effective way to confine the range of object spectral reflectance. In this thesis, a new constraint on spectral reflectance based on the relative ratios of the parameters from principal component analysis (PCA) decomposition is proposed. The proposed constraint was applied to illuminant detection methods as a metric on the recovered spectral reflectance. Because of the importance of the sensor sensitivities and their wide variation, the influence from the sensor sensitivities on different kinds of illuminant estimation methods was also studied. Estimation method stability to wrong sensor information was tested, suggesting the possible solution to illuminant estimation on images with unknown sources. In addition, with the development of multi-channel imaging, some research on illuminant estimation for multi-channel images both on the correlated color temperature (CCT) estimation and the illuminant spectral recovery was performed in this thesis. All the improvement and new proposed methods in this thesis are tested and compared with those existing methods with best performance, both on synthetic data and real images. The comparison verified the high efficiency and implementation simplicity of the proposed methods

Evaluating color texture descriptors under large variations of controlled lighting conditions

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.Comment: Submitted to the Journal of the Optical Society of America

Bootstrapping Color Constancy

Bootstrapping provides a novel approach to training a neural network to estimate the chromaticity of the illuminant in a scene given image data alone. For initial training, the network requires feedback about the accuracy of the network’s current results. In the case of a network for color constancy, this feedback is the chromaticity of the incident scene illumination. In the past1, perfect feedback has been used, but in the bootstrapping method feedback with a considerable degree of random error can be used to train the network instead. In particular, the grayworld algorithm2, which only provides modest color constancy performance, is used to train a neural network which in the end performs better than the grayworld algorithm used to train it

Reliable identification by color under natural conditions

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. Keywords: color vision, color constancy, color naming, object recognition, natural environment Citation: Granzier, J. J. M., Brenner, E., & Smeets, J. B. J. (2009). Reliable identification by color under natural conditions. Journal of Vision, 9(1):39, 1-8, http://journalofvision.org/9/1/39/, doi:10.1167/9.1.39. Introduction The light that is reflected from an illuminated object depends both on its surfaces' reflectance properties and on the illumination of the scene. If we are interested in the object's reflectance properties the fact that the illumination can vary drastically over time and between locations raises a problem for our visual system, since the intensity and spectral distribution of the light that is reflected from the object in question onto the receptors in our eyes will also vary considerably Probably many factors are involved in achieving color constancy, including various kinds of spatial (e.g., The extent to which color constancy is achieved differs between studies, probably because factors such as overall scene complexity Methods Subjects 21 subjects (including two of the authors) with normal color vision Procedure For practical reasons, the experiment was performed in three groups of seven subjects. During an 'instruction phase' subjects were told how to name the colors of six different test papers that were presented simultaneously on a desk under daylight illumination (see Subjects were not told that each paper would be presented 4 times. They wrote the name of the color of the paper that they thought was being shown to them on an answer form ("white," "gray," "green," "red," "blue" or "yellow"). At each location, the experimenter presented the test paper separately to each subject. Subjects were allowed to hold the test paper in their hands and change its orientation. They were allowed to look around as they pleased, so they could compare the test paper's color to the colors of objects in the direct vicinity, but were not allowed to compare the color of the test paper with their white answer form, and they had to remain at the place at which the experimenter had given them the test paper. Subjects were not allowed to talk about the experiment during the tour and were instructed to keep their answer form hidden from the other participants. The locations We used both indoor and outdoor locations (see examples in Journal of Vision Baseline measurement Although the difference between the papers was very clear when they were presented simultaneously, identifying them in isolation was quite difficult. In a separate measurement, we tested our subjects' ability to identify the test papers at a fixed place under constant fluorescent illumination (Philips, 38 HF; 50 watt). Five subjects who also participated in the main experiment took part in this baseline measurement. The CIE xy coordinates of the light reflected by the test papers under these conditions, as measured with a Minolta CS-100A chroma meter, were (0.436, 0.404), (0.432, 0.406), (0.439, 0.402), (0.426, 0.401), (0.441, 0.411) and (0.436, 0.405), for the gray, green, red, blue, yellow and white test paper respectively. The procedure was similar to that of the main experiment, but the background was always the same (the gray surface of a table), the illumination did not change between the first simultaneous presentation and the subsequent test presentations, and subjects remained at the same place under constant illumination between the presentations. Thus, performance is unlikely to be limited by failures of color constancy. After presenting all six pieces of paper simultaneously, the experimenter placed one of the six test papers on the table every three minutes, and the subjects had to write down which paper they thought was being presented (i.e. its color). As in the main experiment, each test paper was presented four times, and the papers were presented in random order (24 trials). The three minutes waiting time was chosen to match the time between judgments in the main experiment. Subjects remained in the room during the 3 minutes between presentations. Analysis To illustrate the judgments that subjects made, pie charts of the groups of 7 subjects' responses were made per location and test paper. The corresponding color of the reflected light was indicated for each pie chart. Since subjects could move the papers around and the illumination could change slightly while the members of the group sequentially made their judgments, we measured the color of the reflected light several times at each location for each group (while the subjects were making their decisions) and calculated the average CIE xyY values. These averages are shown together with the abovementioned pie charts. The variability between these repeated measurements turned out to be quite small (the median standard deviations while a paper was shown to the 7 subjects of the second group were 0.002, 0.001 and 12.3% for CIE x, y and Y, respectively). That performance would not be perfect is obvious because we chose shades of colors that were difficult to distinguish. The question is to what extent performance is worse when the papers are shown at various locations with different kinds of illuminations than when the papers were shown at a single location under a fixed illumination (baseline). To find out, we plotted the percentage of correct responses as a function of the distance in CIE color space between the test papers' CIE xy coordinates during the experiment and during the corresponding instruction phase. We averaged consecutive groups of 6 presentations (a presentation is a set of 7 responses for a given combination of paper and illumination) after sorting the presentations in terms of the above-mentioned distance. Results The average number of correct responses during the main experiment was 55.8% (ranging between 37.5% and 79.2% for individual subjects; 16.6% is chance level). During the baseline measurement, in which there was no change in illumination or in background color and the subjects were fully adapted to the illumination, 87.5% of the responses were correct. That subjects made error

The science of color and color vision

A survey of color science and color vision

Illumination Estimation from Dichromatic Planes

Adopting the dichromatic reflection model under the assumption of neutral interface reflection, the color of the illuminating light can be estimated by intersecting the planes that the color response of two or more different materials describe. From the color response of any given region, most approaches estimate a single plane on the assumption that only a single material is imaged. This assumption, however, is often violated in cluttered scenes. In this paper, rather than a single planar model, several coexisting planes are used to explain the observed color response. In estimating the illuminant, a set of candidate lights is assessed for goodness of fit given the assumed number of coexisting planes. The candidate light giving the minimum error fit is then chosen as representative of the scene illuminant. The performance of the proposed approach is explored on real images

Automated detection of effective scene illuminant chromaticity from specular highlights in digital images

An advanced, automated method is presented for determining an effective scene illuminant chromaticity (scene illuminant plus imaging system variables) from specular highlights in digital images subsequent to image capture. Underlying theories are presented based on a two component reflection model where the scene illuminant relative spectral power distribution is preserved in the specular component. Related methodologies for extracting scene illuminant information as well as alternative methods for achieving color constancy are presented along with factors which inhibit successful implementation. Following, development of a more robust algorithm is discussed. This algorithm is based on locating the center of convergence of a radial line pattern in the two-dimensional chromaticity histogram which theoretically identifies the effective scene illuminant chromaticity. This is achieved by using a radiality index to quantify the relative correlation between a radial mask and the histogram radial line pattern at discrete chromaticity coordinates within a specified search region. The coordinates associated with the strongest radiality index are adopted to represent the effective scene illuminant chromaticity. For a set of controlled test images, the physics-based specular highlight algorithm determined effective scene illuminant chromaticities to a level of accuracy which was nearly three times better than that of a benchmark statistically-based gray-world algorithm. The primary advantage of the specular highlight algorithm was its sustained performance when presented with image conditions of dominant colors, weak specular reflections, and strong interreflections

Colour constancy in simple and complex scenes

PhD ThesisColour constancy is defined as the ability to perceive the surface colours of objects within scenes as approximately constant through changes in scene illumination. Colour constancy in real life functions so seamlessly that most people do not realise that the colour of the light emanating from an object can change markedly throughout the day. Constancy measurements made in simple scenes constructed from flat coloured patches do not produce constancy of this high degree. The question that must be asked is: what are the features of everyday scenes that improve constancy? A novel technique is presented for testing colour constancy. Results are presented showing measurements of constancy in simple and complex scenes. More specifically, matching experiments are performed for patches against uniform and multi-patch backgrounds, the latter of which provide colour contrast. Objects created by the addition of shape and 3-D shading information are also matched against backgrounds consisting of matte reflecting patches. In the final set of experiments observers match detailed depictions of objects - rich in chromatic contrast, shading, mutual illumination and other real life features - within depictions of real life scenes. The results show similar performance across the conditions that contain chromatic contrast, although some uncertainty still remains as to whether the results are indicative of human colour constancy performance or to sensory match capabilities. An interesting division exists between patch matches performed against uniform and multi-patch backgrounds that is manifested as a shift in CIE xy space. A simple model of early chromatic processes is proposed and examined in the context of the results