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Natural illumination is a mixture of sunlight and skylight, modified by interactions with atmospheric particles and interreflections between physical surfaces. Unlike traditional artificial light sources, natural illumination is spectrally dynamic, changing over short and long timescales. Over the day, daylight's correlated color temperature typically ranges from cool (~12,000K) to warm (~2000K), following the well-defined daylight chromaticity locus. Analysis of existing spectral irradiance databases and new measurements reveal a characteristic tripartite pattern: for chromaticity, the periods of fastest change occur in early morning and late evening at the lowest irradiances, with an interim period of relative stability. Illuminance changes are governed systematically by changes in solar elevation and tend to be fastest at the day's extremities, but unsystematic largely weather-related factors perturb this smooth trajectory. The human visual and nonvisual responses to light evolved under these changes. Psychophysical studies demonstrate that the threshold visibility of temporal changes in global illumination chromaticity depends on the chromatic direction of change and adapting chromaticity, with the lowest sensitivity occurring for changes towards neutral. Comparisons with the measured systematic changes in natural illumination suggests that the latter are too slow to be directly detected. A speculation is that visual mechanisms dampen sensitivity to the largest natural changes in illumination chromaticity in order to maintain perceptual stability of object color. Nonvisual mechanisms appear tuned to chromaticity changes at dawn and dusk, and hence are critical for syncing the circadian clock with environmental conditions. The latter might also feed long-term memory of illumination conditions as well as subjective experiences of illumination atmosphere.

The colors that people see depend not only on the surface properties of objects but also on how these properties interact with light as well as on how light reflected from objects interacts with an individual’s visual system. Because individual visual systems vary, the same visual stimulus may elicit different perceptions from different individuals. #thedress phenomenon drove home this point: different individuals viewed the same image and reported it to be widely different colors: blue and black versus white and gold. This phenomenon inspired a collection of demonstrations presented at the Vision Sciences Society 2015 Meeting which showed how spatial and temporal manipulations of light spectra affect people’s perceptions of material colors and illustrated the variability in individual color perception. The demonstrations also explored the effects of temporal alterations in metameric lights, including Maxwell’s Spot, an entoptic phenomenon. Crucially, the demonstrations established that #thedress phenomenon occurs not only for images of the dress but also for the real dress under real light sources of different spectral composition and spatial configurations.

Purpose: Optical coherence tomography (OCT) has recently emerged as a source for powerful biomarkers in neurodegenerative diseases such as multiple sclerosis (MS) and neuromyelitis optica (NMO). The application of machine learning techniques to the analysis of OCT data has enabled automatic extraction of information with potential to aid the timely diagnosis of neurodegenerative diseases. These algorithms require large amounts of labeled data, but few such OCT data sets are available now. Methods: To address this challenge, here we propose a synthetic data generation method yielding a tailored augmentation of three-dimensional (3D) OCT data and preserving differences between control and disease data. A 3D active shape model is used to produce synthetic retinal layer boundaries, simulating data from healthy controls (HCs) as well as from patients with MS or NMO. Results: To evaluate the generated data, retinal thickness maps are extracted and evaluated under a broad range of quality metrics. The results show that the proposed model can generate realistic-appearing synthetic maps. Quantitatively, the image histograms of the synthetic thickness maps agree with the real thickness maps, and the cross-correlations between synthetic and real maps are also high. Finally, we use the generated data as an augmentation technique to train stronger diagnostic models than those using only the real data. Conclusions: This approach provides valuable data augmentation, which can help overcome key bottlenecks of limited data. Translational Relevance: By addressing the challenge posed by limited data, the proposed method helps apply machine learning methods to diagnose neurodegenerative diseases from retinal imaging.

Previous studies suggest that to achieve color constancy, the human visual system makes use of multiple cues, including a priori assumptions about the illumination (“daylight priors”). Specular highlights have been proposed to aid constancy, but the evidence for their usefulness is mixed. Here, we used a novel cue-combination approach to test whether the presence of specular highlights or the validity of a daylight prior improves illumination chromaticity estimates, inferred from achromatic settings, to determine whether and under which conditions either cue contributes to color constancy. Observers made achromatic settings within three-dimensional rendered scenes containing matte or glossy shapes, illuminated by either daylight or nondaylight illuminations. We assessed both the variability of these settings and their accuracy, in terms of the standard color constancy index (CCI). When a spectrally uniform background was present, neither CCIs nor variability improved with specular highlights or daylight illuminants (Experiment 1). When a Mondrian background was introduced, CCIs decreased overall but were higher for scenes containing glossy, as opposed to matte, shapes (Experiments 2 and 3). There was no overall reduction in variability of settings and no benefit for scenes illuminated by daylights. Taken together, these results suggest that the human visual system indeed uses specular highlights to improve color constancy but only when other cues, such as from the local surround, are weakened.

Studying color preferences provides a means to discover how perceptual experiences map onto cognitive and affective judgments. A challenge is finding a parsimonious way to describe and predict patterns of color preferences, which are complex with rich individual differences. One approach has been to model color preferences using factors from metric color spaces to establish direct correspondences between dimensions of color and preference. Prior work established that substantial, but not all, variance in color preferences could be captured by weights on color space dimensions using multiple linear regression. The question we address here is whether model fits may be improved by using different color metric specifications. We therefore conducted a large-scale analysis of color space models, and focused in-depth analysis on models that differed in color space (cone-contrast vs. CIELAB), coordinate system within the color space (Cartesian vs. cylindrical), and factor degrees (1st degree only, or 1st and 2nd degree). We used k-fold cross validation to avoid over-fitting the data and to ensure fair comparisons across models. The best model was the 2nd-harmonic Lch model ("LabC Cyl2"). Specified in CIELAB space, it included 1st and 2nd harmonics of hue (capturing opponency in hue preferences and simultaneous liking/disliking of both hues on an opponent axis, respectively), lightness, and chroma. These modeling approaches can be used to characterize and compare patterns for group averages and individuals in future datasets on color preference, or other measures in which correspondences between color appearance and cognitive or affective judgments may exist.

Psychiatric inpatient units often maintain a degree of lighting at night to facilitate the observation of patients, but this has the potential to disrupt sleep. Certain wavelengths of light may be less likely to disturb sleep and if such lighting permitted adequate observations, patient wellbeing may be improved. AIMS AND METHOD This study explored the effects of changing night-lights from broad-band white to narrow-band red on the amount of sleep observed, ‘as required’ medication administered and number of falls, in an old age psychiatry inpatient setting. Qualitative data was also gathered with a staff questionnaire. We hypothesised that compared to the use of white lights, red lights would be associated with a greater amount of recorded sleep, lesser use of ‘as required’ medication and no increase in the number of falls (reflecting comparable safety). RESULTS Whilst there were no significant differences in quantitative measures recorded, there were more observations of sleep during the red light period than the white light period (14.1 versus 13.9 times per night) (U = 627.5, z = − 0.69, p = 0.49) and fewer ‘as required’ medication administrations during the red light period compared to the white light period (3.3 versus 4.8 times per night) (U = 640.0, z = 0.56, p = 0.57). Qualitatively, the staff of the organic assessment unit reported that patients were sleeping better and less agitated at night. CLINICAL IMPLICATIONS Larger and more in-depth studies are required to examine the full effectiveness of using safe, sleep-enhancing lighting on wards at night.

We measured discrimination thresholds for illumination changes along different chromatic directions starting from chromatically biased reference illuminations. Participants viewed a Mondrian-papered scene illuminated by LED lamps. The scene was first illuminated by a reference illumination, followed by two comparisons. One comparison matched the reference (the target); the other (the test) varied from the reference, nominally either bluer, yellower, redder, or greener. The participant's task was to correctly select the target. A staircase procedure found thresholds for discrimination of an illumination change along each axis of chromatic change. Nine participants completed the task for five different reference illumination conditions (neutral, blue, yellow, red, and green). We find that relative discrimination thresholds for different chromatic directions of illumination change vary with the reference illumination. For the neutral reference, there is a trend for thresholds to be highest in the bluer illumination-change direction, replicating our previous reports of a "blue bias" for neutral reference illuminations. For the four chromatic references (blue, yellow, red, and green), the change in illumination toward the neutral reference is less well discriminated than changes in the other directions: a "neutral bias." The results have implications for color constancy: In considering the stability of surface appearance under changes in illumination, both the starting chromaticity of the illumination and direction of change must be considered, as well as the chromatic characteristics of the surface reflectance ensemble. They also suggest it will be worthwhile to explore whether and how the human visual system has internalized the statistics of natural illumination changes.

Temporal changes in illumination are ubiquitous; natural light, for example, varies in color temperature and irradiance throughout the day. Yet little is known about human sensitivity to temporal changes in illumination spectra. Here, we aimed to determine the minimum detectable velocity of chromaticity change of daylight metamers in an immersive environment. The main stimulus was a continuous, monotonic change in global illumination chromaticity along the daylight locus in warmer (toward lower correlated color temperatures [CCTs]) or cooler directions, away from an adapting base light (CCT: 13,000 K, 6500 K, 4160 K, or 2000 K). All lights were generated by spectrally tunable overhead lamps as smoothest-possible metamers of the desired chromaticities. Mean detection thresholds (for 22 participants) for a fixed duration of 10 seconds ranged from 15 to 2 CIELUV ΔE units, depending significantly on base light CCT and with a significant interaction between CCT and direction of change. Cool changes become less noticeable for progressively warmer base lights and vice versa. For the two extreme base lights, sensitivity to changes toward neutral is significantly lower than for the opposite direction. The results suggest a "neutral bias" in illumination change discriminability, and that typical temporal changes in daylight chromaticity are likely to be below threshold detectability, at least where there are no concomitant overall illuminance changes. These factors may contribute to perceptual stability of natural scenes and color constancy.

In order to characterize cortical responses to coherent motion we use magnetoencephalography (MEG) to measure human brain activity that is modulated by the degree of global coherence in a visual motion stimulus. Five subjects passively viewed two-phase motion sequences of sparse random dot fields. In the first (incoherent) phase the dots moved in random directions; in the second (coherent) phase a variable percentage of dots moved uniformly in one direction while the others moved randomly. We show that: (i) visual-motion-evoked magnetic fields, measured with a whole-scalp neuromagnetometer, reveal two transient events, within which we identify two significant peaks--the 'ON-M220' peak approximately 220 ms after the onset of incoherent motion and the 'TR-M230' peak, approximately 230 ms after the transition from incoherent to coherent motion; (ii) in lateral occipital channels, the TR-M230 peak amplitude varies with the percentage of motion coherence; (iii) two main sources are active in response to the transition from incoherent to coherent motion, the human medial temporal area complex/V3 accessory area (hMT+/V3A) and the superior temporal sulcus (STS), and (iv) these distinct areas show a similar, significant dependence of response strength and latency on motion coherence.

The disagreement between people who named #theDress (the Internet phenomenon of 2015) "blue and black" versus "white and gold" is thought to be caused by individual differences in color constancy. It is hypothesized that observers infer different incident illuminations, relying on illumination "priors" to overcome the ambiguity of the image. Different experiences may drive the formation of different illumination priors, and these may be indicated by differences in chronotype. We assess this hypothesis, asking whether matches to perceived illumination in the image and/or perceived dress colors relate to scores on the morningness-eveningness questionnaire (a measure of chronotype). We find moderate correlations between chronotype and illumination matches (morning types giving bluer illumination matches than evening types) and chronotype and dress body matches, but these are significant only at the 10% level. Further, although inferred illumination chromaticity in the image explains variation in the color matches to the dress (confirming the color constancy hypothesis), color constancy thresholds obtained using an established illumination discrimination task are not related to dress color perception. We also find achromatic settings depend on luminance, suggesting that subjective white point differences may explain the variation in dress color perception only if settings are made at individually tailored luminance levels. The results of such achromatic settings are inconsistent with their assumed correspondence to perceived illumination. Finally, our results suggest that perception and naming are disconnected, with observers reporting different color names for the dress photograph and their isolated color matches, the latter best capturing the variation in the matches.

Colour constancy is typically weaker in the laboratory than it seems in our everyday experience. New measurements using real-world stimuli show that colour constancy is in fact almost perfect, and that several different perceptual mechanisms contribute to achieving it.

Image segmentation is an important early stage in visual processing, in which the visual system groups together parts of the image that belong together, prior to or in conjunction with object recognition. Two principal processes may be involved in image segmentation: an edge–based process that uses feature contrasts to mark boundaries of coherent regions, and a region–based process that groups similar features over a larger scale. Earlier, we have shown that motion and colour interact strongly in image segmentation by the human visual system. Here we explore the nature of this interaction in terms of edge– and region–based processes. We measure performance on a region–based colour segmentation task in the presence of distinct types of motion information, in the form of edges and regions which in themselves do not reveal the location of the colour target. The results show that both motion edges and regions may guide the integrative process required for this colour segmentation task. Motion edges appear to act by delimiting areas over which to integrate colour information, whereas motion similarities define primitive surfaces within which colour grouping and segmentation processes are deployed.

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.

Our perception of a surface's colour depends in a complex way on the colours of surrounding surfaces; this ‘colour contrast' is linked to the ‘colour constancy' which is important for object recognition, but in ways more complex than we yet understand.

Theories of image segmentation suggest that the human visual system may use two distinct processes to segregate figure from background: a local process that uses local feature contrasts to mark borders of coherent regions and a global process that groups similar features over a larger spatial scale. We performed psychophysical experiments to determine whether and to what extent the global similarity process contributes to image segmentation by motion and color. Our results show that for color, as well as for motion, segmentation occurs first by an integrative process on a coarse spatial scale, demonstrating that for both modalities the global process is faster than one based on local feature contrasts. Segmentation by motion builds up over time, whereas segmentation by color does not, indicating a fundamental difference between the modalities. Our data suggest that segmentation by motion proceeds first via a cooperative linking over space of local motion signals, generating almost immediate perceptual coherence even of physically incoherent signals. This global segmentation process occurs faster than the detection of absolute motion, providing further evidence for the existence of two motion processes with distinct dynamic properties.

Characterizing humans' ability to discriminate changes in illumination provides information about the visual system's representation of the distal stimulus. We have previously shown that humans are able to discriminate illumination changes and that sensitivity to such changes depends on their chromatic direction. Probing illumination discrimination further would be facilitated by the use of computer-graphics simulations, which would, in practice, enable a wider range of stimulus manipulations. There is no a priori guarantee, however, that results obtained with simulated scenes generalize to real illuminated scenes. To investigate this question, we measured illumination discrimination in real and simulated scenes that were well-matched in mean chromaticity and scene geometry. Illumination discrimination thresholds were essentially identical for the two stimulus types. As in our previous work, these thresholds varied with illumination change direction. We exploited the flexibility offered by the use of graphics simulations to investigate whether the differences across direction are preserved when the surfaces in the scene are varied. We show that varying the scene's surface ensemble in a manner that also changes mean scene chromaticity modulates the relative sensitivity to illumination changes along different chromatic directions. Thus, any characterization of sensitivity to changes in illumination must be defined relative to the set of surfaces in the scene.

New visual illusions provide further evidence for the influence of higher-order analyses of the visual scene on the perceived brightnesses of surfaces within that scene.

Adult colour preference has been summarized quantitatively in terms of weights on the two fundamental neural processes that underlie early colour encoding: the S-(L+M) ('blue-yellow') and L-M ('red-green') cone-opponent contrast channels (Ling, Hurlbert & Robinson, 2006; Hurlbert & Ling, 2007). Here, we investigate whether colour preference in 4-5-month-olds may be analysed in the same way. We recorded infants' eye-movements in response to pairwise presentations of eight colour stimuli varying only in hue. Infants looked longest at reddish and shortest at greenish hues. Analyses revealed that the L-M and S-(L+M) contrast between stimulus colour and background explained around half of the variation in infant preference across the hue spectrum. Unlike adult colour preference patterns, there was no evidence for sex differences in the weights on either of the cone-opponent contrast components. The findings provide a quantitative model of infant colour preference that summarizes variation in infant preference across hues.

Summary The long history of color preference studies has been described as "bewildering, confused and contradictory" [1]. Although recent studies [1–3] tend to agree on a universal preference for ‘blue', the variety and lack of control in measurement methods have made it difficult to extract a systematic, quantitative description of preference. Furthermore, despite abundant evidence for sex differences in other visual domains, and specifically in other tasks of color perception [4,5], there is no conclusive evidence for the existence of sex differences in color preference. This fact is perhaps surprising, given the prevalence and longevity of the notion that little girls differ from boys in preferring ‘pink' [6]. Here we report a robust, cross-cultural sex difference in color preference, revealed by a rapid paired-comparison task. Individual color preference patterns are summarized by weights on the two fundamental neural dimensions that underlie color coding in the human visual system. We find a consistent sex difference in these weights, which, we suggest, may be linked to the evolution of sex-specific behavioral uses of trichromacy.

Color constancy--by which objects tend to appear the same color under changes in illumination--is most likely achieved by several mechanisms, operating at different levels in the visual system. One powerful contributory mechanism is simultaneous spatial color contrast. Under changes in natural illumination the spatial ratios of within-type cone excitations between natural surfaces tend to be preserved (Foster and Nascimento, 1994); therefore, the neural encoding of colors as spatial contrasts tends to achieve constancy. Several factors are known to influence the strength of chromatic contrast induction between surfaces, including their relative luminance, spatial scale, spatial configuration and context (Ware and Cowan, 1982; Zaidi et al., 1991). Here we test the hypothesis that color contrast is weakened by differences between surfaces which indicate that they may be under distinct illuminants. We summarize psychophysical measurements of the effects of relative motion, relative depth and texture differences on chromatic contrast induction. Of these factors, only texture differences between surfaces weaken chromatic contrast induction. We also consider neurophysiological and neuropsychological evidence and conclude that the mechanisms which mediate local chromatic contrast effects are sited at low levels in the visual system, in primary visual cortex (V1) or below, prior to image segmentation mechanisms which require computation of relative depth or motion. V1 and lower areas may therefore play a larger role in color constancy than previously thought.

The traditional achromatic Mach card effect is an example of lightness inconstancy and a demonstration of how shape and lightness perception interact. We present a quantitative study of this phenomenon and explore the conditions under which it occurs. The results demonstrate that observers show lightness constancy only when sufficient information is available about the light-source position, and the perceptual task required of them is surface identification rather than direct colour-appearance matching. An analysis and comparison of these results with the chromatic Mach card effect (Bloj et al 1999 Nature402 877–879) demonstrate that the luminance effects of mutual illumination do not account for the change in lightness perception in the traditional Mach card.

One proposed mechanism for underpinning colour constancy is computation of the relative activity of cones within one class – cone ratios, or cone contrasts – between surfaces in a fixed scene undergoing a change in illuminant. Although there is evidence that cone ratios do determine colour appearance under many conditions, the site or sites of their computation is unknown. Here, we report that a cerebrally achromatopsic observer, MS, displayed evidence of colour constancy in asymmetric colour matching tasks and was able to discriminate changes in cone ratios for simple, but not complex scenes. We hypothesise that the site of local cone-ratio computation is therefore early in the visual system, probably retinal.

Colour constancy is typically measured with techniques involving asymmetric matching by adjustment, in which the observer views two scenes under different illuminants and adjusts the colour of a reference patch in one to match a test patch in the other. This technique involves an unnatural task, requiring the observer to predict and adjust colour appearance under an illumination shift. Natural colour constancy is more a simple matter of determining whether a colour is the same as or different from that seen under different illumination conditions. There are also technical disadvantages to the method of matching by adjustment, particularly when used to measure colour constancy in complex scenes. Therefore, we have developed and tested a two-dimensional method of constant-stimuli, forced-choice matching paradigm for measuring colour constancy. Observers view test and reference scenes haploscopically and simultaneously, each eye maintaining separate adaptation throughout a session. On each trial, a pair of test and reference patches against multicoloured backgrounds are presented, the reference patch colours being selected from a two-dimensional grid of displayable colours around the point of perfect colour constancy. The observer's task is to respond “same” or “different”. Fitting a two-dimensional Gaussian to the percentage of “different” responses yields (1) the subjective colour-constancy point, (2) the discrimination ellipse centred on this point, and (3) a map of changes in sensitivity to chromatic differences induced by the illuminant shift. The subjective colour-constancy point measured in this way shows smaller deviations from perfect colour constancy—under conditions of monocular adaptation—than previously reported; discrimination ellipses are several times larger than standard MacAdam ellipses; and chromatic sensitivity is independent of the direction of the illuminant shift, for broad distributions of background colours.

This report describes the main features of a view-based model of object recognition. The model does not attempt to account for specific cortical structures; it tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network (RBF-like; see Poggio and Girosi, 1989; Poggio, 1990) in which each of the hidden units is broadly tuned to a specific view of the object to be recognized. The network output, which may be largely view independent, is first described in terms of some simple simulations. The following refinements and details of the basic module are then discussed: (1) some of the units may represent only components of views of the object—the optimal stimulus for the unit its “center,” is effectively a complex feature; (2) the units' properties are consistent with the usual description of cortical neurons as tuned to multidimensional optimal stimuli and may be realized in terms of plausible biophysical mechanisms; (3) in learning to recognize new objects, preexisting centers may be used and modified, but also new centers may be created incrementally so as to provide maximal view invariance; (4) modules are part of a hierarchical structure—the output of a network may be used as one of the inputs to another, in this way synthesizing increasingly complex features and templates; (5) in several recognition tasks, in particular at the basic level, a single center using view-invariant features may be sufficient. Modules of this type can deal with recognition of specific objects, for instance, a specific face under various transformations such as those due to viewpoint and illumination, provided that a sufficient number of example views of the specific object are available. An architecture for 3D object recognition, however, must cope- to some extent—even when only a single model view is given. The main contribution of this report is an outline of a recognition architecture that deals with objects of a nice class undergoing a broad spectrum of transformations—due to illumination, pose, expression, and so on- by exploiting prototypical examples. A nice class of objects is a set of objects with sufficiently similar transformation properties under specific transformations, such as viewpoint transformations. For nice object classes, we discuss two possibilities: (1) class-specific transformations are to be applied to a single model image to generate additional virtual example views, thus allowing some degree of generalization beyond what a single model view could otherwise provide; (2) class-specific, view-invariant features are learned from examples of the class and used with the novel model image, without an explicit generation of virtual examples.

Image gradients--smooth changes in color and luminance--may be caused by intrinsic surface reflectance properties or extrinsic illumination phenomena, including shading, shadowing, and inter-reflections. In turn, image gradients may provide the visual system with information concerning the origin of these factors, such as the orientation of surfaces with respect to the light source. Color gradients induced by mutual illumination (MI) may play a similar role to that of luminance gradients in shape-from-shading algorithms; it has been shown that 3D shape perception modulates the influence of MI on surface color perception (M. G. Bloj, D. Kersten, & A. C. Hurlbert, 1999). In this study, we assess human sensitivity to changes in color and luminance gradients that arise from changes in the light source position, within a complex scene. In Experiment 1, we tested whether observers were able to discriminate between gradients due to different light source positions. We found that observers reliably detected a change in the gradient information when the light source position differed by only 4 deg from the reference scene. This sensitivity was mainly based on the luminance information in the gradient (Experiments 2 and 3). Some observers make use of the spatial distribution of chromaticity and luminance values within gradients when discriminating between them (Experiment 4). The high sensitivity to gradient differences supports the notion that gradients contain information that may assist in the recovery of 3D shape and scene configuration properties.

We investigate color constancy for real 2D paper samples using a successive matching paradigm in which the observer memorizes a reference surface color under neutral illumination and after a temporal interval selects a matching test surface under the same or different illumination. We find significant effects of the illumination, reference surface, and their interaction on the matching error. We characterize the matching error in the absence of illumination change as the "pure color memory shift" and introduce a new index for successive color constancy that compares this shift against the matching error under changing illumination. The index also incorporates the vector direction of the matching errors in chromaticity space, unlike the traditional constancy index. With this index, we find that color constancy is nearly perfect.

In the natural world, objects are characterized by a variety of attributes, including color and shape. The contributions of these two attributes to object recognition are typically studied independently of each other, yet they are likely to interact in natural tasks. Here we examine whether color and size (a component of shape) interact in a real three-dimensional (3D) object similarity task, using solid domelike objects whose distinct apparent surface colors are independently controlled via spatially restricted illumination from a data projector hidden to the observer. The novel experimental setup preserves natural cues to 3D shape from shading, binocular disparity, motion parallax, and surface texture cues, while also providing the flexibility and ease of computer control. Observers performed three distinct tasks: two unimodal discrimination tasks, and an object similarity task. Depending on the task, the observer was instructed to select the indicated alternative object which was "bigger than," "the same color as," or "most similar to" the designated reference object, all of which varied in both size and color between trials. For both unimodal discrimination tasks, discrimination thresholds for the tested attribute (e.g., color) were increased by differences in the secondary attribute (e.g., size), although this effect was more robust in the color task. For the unimodal size-discrimination task, the strongest effects of the secondary attribute (color) occurred as a perceptual bias, which we call the "saturation-size effect": Objects with more saturated colors appear larger than objects with less saturated colors. In the object similarity task, discrimination thresholds for color or size differences were significantly larger than in the unimodal discrimination tasks. We conclude that color and size interact in determining object similarity, and are effectively analyzed on a coarser scale, due to noise in the similarity estimates of the individual attributes, inter-attribute attentional interactions, or coarser coding of attributes at a "higher" level of object representation.

SummaryA widely-viewed image of a dress elicits striking individual variation in colour perception. Experiments with multiple variants of the image suggest that the individual differences may arise through the action of visual mechanisms that normally stabilise object colour.

Background: An important early stage in visual processing is image segmentation, in which similar regions are grouped together and segregated from dissimilar regions, so that distinct objects ultimately may be located and recognized. In the natural world, objects are simultaneously characterized by colour, motion, texture and other visual attributes. How does the human visual system combine these attributes to segment the image? Although colour and motion information are conveyed by distinct functional streams from retina to visual cortex, there is increasing evidence for early and substantial cross-talk between the streams. Here, we explore psychophysical evidence for interactions between colour and motion in image segmentation. Results: Observers performed forced-choice segmentation tasks on random-dot stimuli. The dots in the vertical target figure were distinguished from the background dots by a different distribution of speeds or colours. To explore interactions between motion and colour segmentation, we added motion noise to the colour signal (or vice versa ) by assigning all dots speeds (or colours) drawn from one of several noise distributions. Motion noise severely affects segmentation by colour. Motion noise defined by a broad distribution of speeds degrades colour segmentation, but a two-speed motion distribution (half moving up, half moving down) facilitates colour segmentation. Control experiments prove that the facilitatory effect is not caused by integrating colour information over different frames, nor can it be explained by probability summation over the two planes of moving dots. Colour noise also affects motion segmentation, but under a more restricted range of conditions, and not in a facilitatory way. Conclusions: Colour and motion information interact at early stages during image segmentation, before decisions based on either cue in isolation are made. The robust bipolar effects of motion information on segmentation by colour indicate that the establishment of motion-defined surfaces takes primacy, and that such surfaces constitute important primitives for further processing.

New studies show that perceptual learning does not reduce the noise inherent to the neuronal mechanisms of perception. Rather, learning boosts the brain's capability to extract and make use of the relevant outside signal – but where and how the neuronal changes occur is still unknown.

A lightness algorithm that separates surface reflectance from illumination in a Mondrian world is synthesized automatically from a set of examples, which consist of pairs of input (intensity signal) and desired output (surface reflectance) images. The algorithm, which resembles a new lightness algorithm recently proposed by Land, is approximately equivalent to filtering the image through a center-surround receptive field in individual chromatic channels. The synthesizing technique, optimal linear estimation, requires only one assumption, that the operator that transforms input into output is linear. This assumption is true for a certain class of early vision algorithms that may therefore be synthesized in a similar way from examples. Other methods of synthesizing algorithms from examples, or "learning," such as back-propagation, do not yield a significantly better lightness algorithm.

Objects in the natural world possess different visual attributes, including shape, colour, surface texture and motion. Previous perceptual studies have assumed that the brain analyses the colour of a surface independently of its three-dimensional shape and viewing geometry, although there are neural connections between colour and two-dimensional form processing early in the visual pathway. Here we show that colour perception is strongly influenced by three-dimensional shape perception in a novel, chromatic version of the Mach Card--a concave folded card with one side made of magenta paper and the other of white paper. The light reflected from the magenta paper casts a pinkish glow on the white side. The perceived colour of the white side changes from pale pink to deep magenta when the perceived shape of the card flips from concave to convex. The effect demonstrates that the human visual system incorporates knowledge of mutual illumination-the physics of light reflection between surfaces--at an early stage in colour perception.

The computational problem underlying color vision is to recover the invariant surface-spectral-reflectance properties of an object. Lightness algorithms, which recover an approximation to surface reflectance in independent wavelength channels, have been proposed as one method to compute color. This paper clarifies and formalizes the lightness problem by proposing a new formulation of the intensity equation on which lightness algorithms are based and by identifying and discussing two basic subproblems of lightness and color computation: spatial decomposition and spectral normalization of the intensity signal. Several lightness algorithms are reviewed, and a new extension (the multiple-scales algorithm) of one of them is proposed. The main computational result is that each of the lightness algorithms may be derived from a single mathematical formula, under different conditions, which, in turn, imply limitations for the implementation of lightness algorithms by man or machine. In particular, the algorithms share certain limitations on their implementation that follow from the physical constraints imposed on the statement of the problem and the boundary conditions applied in its solution.