Hyperspectral database of fruits and vegetables

Context effects on the perception of saturation of fruit colors in still-life paintings

Still-life painters, especially of the so-called Golden Age (17th century) in the Netherlands, are famous for their masterful techniques of rendering reality. Their amazing abilities to depict different material properties of fruits and flowers are stunning. But how important are these careful arrangements of different objects for the perception of an individual item? Is the perceived color saturation of a single fruit influenced by its surrounding context? We selected fruits in still-life paintings as stimuli to investigate whether and how perceived saturations of fruits were affected by their original contexts. In our study, we focused especially on effects of five context properties: complementary colors, chromatic and luminance contrast, object overlap, and surround variance. Six fruit varieties depicted in high-quality digital reproductions of 48 classic and eight varieties in 64 more recent, modern still-life paintings were selected. In a single trial, eight images of fruits of the same variety appeared on a neutral gray background; half were single fruit cutouts, and the other half were the same fruits embedded in their circular contexts. Fifteen participants ranked all eight images according to perceived color saturations of the fruits. Saturation ratings showed a high agreement of 77%. Surrounding contexts led to an increase in perceived saturation of central fruits. This effect was mainly driven by object overlap, the presence of the central fruit type also in the context, and surround variance. Chroma contrast between fruits and contexts decreased saturation significantly. No significant context effects were found for complementary colors or luminance contrast. Our results show that in paintings, many of the cues that are usually experimentally isolated occur in interesting combinations and lead to an increase in perceived saturation that makes fruit objects more appealing and convincing.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

Color and gloss constancy under diverse lighting environments

When we look at an object, we simultaneously see how glossy or matte it is, how light or dark, and what color. Yet, at each point on the object's surface, both diffuse and specular reflections are mixed in different proportions, resulting in substantial spatial chromatic and luminance variations. To further complicate matters, this pattern changes radically when the object is viewed under different lighting conditions. The purpose of this study was to simultaneously measure our ability to judge color and gloss using an image set capturing diverse object and illuminant properties. Participants adjusted the hue, lightness, chroma, and specular reflectance of a reference object so that it appeared to be made of the same material as a test object. Critically, the two objects were presented under different lighting environments. We found that hue matches were highly accurate, except for under a chromatically atypical illuminant. Chroma and lightness constancy were generally poor, but these failures correlated well with simple image statistics. Gloss constancy was particularly poor, and these failures were only partially explained by reflection contrast. Importantly, across all measures, participants were highly consistent with one another in their deviations from constancy. Although color and gloss constancy hold well in simple conditions, the variety of lighting and shape in the real world presents significant challenges to our visual system's ability to judge intrinsic material properties.

Perception of saturation in natural objects

The distribution of colors across a surface depends on the interaction between its surface properties, its shape, and the lighting environment. Shading, chroma, and lightness are positively correlated: points on the object that have high luminance also have high chroma. Saturation, typically defined as the ratio of chroma to lightness, is therefore relatively constant across an object. Here we explored to what extent this relationship affects perceived saturation of an object. Using images of hyperspectral fruit and rendered matte objects, we manipulated the lightness-chroma correlation (positive or negative) and asked observers which of two objects appeared more saturated. Despite the negative-correlation stimulus having greater mean and maximum chroma, lightness, and saturation than the positive, observers overwhelmingly chose the positive as more saturated. This suggests that simple colorimetric statistics do not accurately represent perceived saturation of objects-observers likely base their judgments on interpretations about the cause of the color distribution.

Sensory representation of surface reflectances: assessments with hyperspectral images

Specifying surface reflectances in a simple and perceptually informative way would be beneficial for many areas of research and application. We assessed whether a 3×3 matrix may be used to approximate how a surface reflectance modulates the sensory color signal across illuminants. We tested whether observers could discriminate between the model's approximate and accurate spectral renderings of hyperspectral images under narrowband and naturalistic, broadband illuminants for eight hue directions. Discriminating the approximate from the spectral rendering was possible with narrowband, but almost never with broadband illuminants. These results suggest that our model specifies the sensory information of reflectances across naturalistic illuminants with high fidelity, and with lower computational cost than spectral rendering.

Modeling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks

We modeled discrimination thresholds for object colors under different lighting environments [J. Opt. Soc. Am. 35, B244 (2018)]. First, we built models based on chromatic statistics, testing 60 models in total. Second, we trained convolutional neural networks (CNNs), using 160,280 images labeled by either the ground-truth or human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.

Why bananas look yellow: The dominant hue of object colours

In this study, we propose a new approach to the perceptual representation of object colours. Three-dimensional objects have a polychromatic colour distribution. Yet, human observers abstract from the variation along the three perceptual colour dimensions when describing objects, such as when we say, "a banana is yellow". We propose that the perceived object colour is determined by the dominant hue. The dominant hue corresponds to the first principal component of an object's chromaticities. Across three experiments, we show for a sample of objects that the chromatic variation away from the dominant hue is almost completely neglected by human observers under non-laboratory viewing conditions. This is partly due to the low visibility of this variation, and partly to attentional change blindness. These findings reveal the potential role of dominant hue in the perception of object colours. Dominant hue may enable us to determine the most representative colours of objects because perceived object colours tend to be maximally bright and saturated. The present findings also imply that we can simplify the colour distributions of objects by projecting them onto their dominant hue. This may be useful for computational applications.

Untrained networks for compressive lensless photography

Compressive lensless imagers enable novel applications in an extremely compact device, requiring only a phase or amplitude mask placed close to the sensor. They have been demonstrated for 2D and 3D microscopy, single-shot video, and single-shot hyperspectral imaging; in each case, a compressive-sensing-based inverse problem is solved in order to recover a 3D data-cube from a 2D measurement. Typically, this is accomplished using convex optimization and hand-picked priors. Alternatively, deep learning-based reconstruction methods offer the promise of better priors, but require many thousands of ground truth training pairs, which can be difficult or impossible to acquire. In this work, we propose an unsupervised approach based on untrained networks for compressive image recovery. Our approach does not require any labeled training data, but instead uses the measurement itself to update the network weights. We demonstrate our untrained approach on lensless compressive 2D imaging, single-shot high-speed video recovery using the camera's rolling shutter, and single-shot hyperspectral imaging. We provide simulation and experimental verification, showing that our method results in improved image quality over existing methods.

Color consistency in the appearance of bleached fabrics

Human observers are remarkably good at perceiving constant object color across illumination changes. However, there are numerous other factors that can modulate surface appearance, such as aging, bleaching, staining, or soaking. Despite this, we are often able to identify material properties across such transformations. Little is known about how and to what extent we can compensate for the accompanying color transformations. Here we investigated whether humans could reproduce the original color of bleached fabrics. We treated 12 different fabric samples with a commercial bleaching product. Bleaching increased luminance and decreased saturation. We presented photographs of the original and bleached samples on a computer screen and asked observers to match the fabric colors to an adjustable matching disk. Different groups of observers produced matches for original and bleached samples. One group of observers were instructed to match the color of the bleached samples as they were before bleaching (i.e., compensate for the effects of bleaching); another, to accurately match color appearance. Observers did compensate significantly for the effects of bleaching when instructed to do so, but not in the appearance match condition. Results of a second experiment suggest that observers achieve color consistency, at least in part, through a strategy based on local spatial differences within the bleached samples. According to the results of a third experiment, these local spatial differences are likely to be the perceptual image cues that allow participants to determine whether a sample is bleached. When the effect of bleaching was limited or uniformly distributed across a sample's surface, observers were uncertain about the bleaching magnitude and seemed to apply cognitive strategies to achieve color consistency.

Lightness Discrimination Depends More on Bright Rather Than Shaded Regions of Three-Dimensional Objects

The brighter portions of a shaded complex object are in principle more informative about its lightness and are preferentially fixated during lightness judgments. In this study, we investigate whether preventing this strategy also has measurable detrimental effects on performance. Observers were presented with a reference and a comparison three-dimensional rendered object and had to choose which one was "painted with a lighter gray." The comparison was rendered with different diffuse reflectance values. We compared precision between three different conditions: full image, 20% of the lightest pixels removed, or 20% of the darkest pixels removed. Removing the bright pixels maximally impaired performance. The results confirm that the strategy of relying on the brightest areas of a complex object in order to estimate lightness is functionally optimal, yielding more precise representations.

Hyperspectral imaging in color vision research: tutorial

This tutorial offers an introduction to terrestrial and close-range hyperspectral imaging and some of its uses in human color vision research. The main types of hyperspectral cameras are described together with procedures for image acquisition, postprocessing, and calibration for either radiance or reflectance data. Image transformations are defined for colorimetric representations, color rendering, and cone receptor and postreceptor coding. Several example applications are also presented. These include calculating the color properties of scenes, such as gamut volume and metamerism, and analyzing the utility of color in observer tasks, such as identifying surfaces under illuminant changes. The effects of noise and uncertainty are considered in both image acquisition and color vision applications.

Disentangling simultaneous changes of surface and illumination

Retinally incident light is an ambiguous product of spectral distributions of light in the environment and their interactions with reflecting, absorbing, and transmitting materials. An ideal color constant observer would unravel these confounded sources of information and account for changes in each factor. Scene statistics have been proposed as a way to compensate for changes in the illumination, but few theories consider changes of 3-dimensional surfaces. Here, we investigated the visual system's capacity to deal with simultaneous changes in illumination and surfaces. Spheres were imaged with a hyperspectral camera in a white box and their colors, as well as that of the illumination were varied along "red-green" and "blue-yellow" axes. Both the original hyperspectral images and replica scenes rendered with Mitsuba were used as stimuli, including rendered scenes with Glavens (Acta Psychologica, 2009, 132, 259-266). Observers viewed sequential, random pairs of our images, with either the whole scene, only the object, or only a part of the background being present. They judged how much the illuminant and object color changed on a scale of 0-100%. Observers could extract simultaneous illumination and reflectance changes when provided with a view of the whole scene, but global scene statistics did not fully account for their behavior, while local scene statistics improved the situation. There was no effect of color axis, shape, or simulated vs. original hyperspectral images. Observers appear to be making use of various sources of local information to complete the task.

The Neon Fruit Illusion: A Fresh Recipe for Colour Science Demonstrations

At this year’s European Conference on Visual Perception, we debuted a novel colour science demonstration—and visual illusion—for the Un mare di illusioni exhibition. Under carefully curated lighting conditions, cycling through different illuminant spectra, certain fruits and vegetables appear to glow and dim in an unchanging environment. Encouraged by the positive reactions it received, and the numerous and specific questions from conference delegates, we here describe what this illusion is, why we believe it may work, and how this particular low-cost setup may be assembled and demonstrated for the amazement of your friends, students, and members of the public.

Computational luminance constancy from naturalistic images

The human visual system supports stable percepts of object color even though the light that reflects from object surfaces varies significantly with the scene illumination. To understand the computations that support stable color perception, we study how estimating a target object's luminous reflectance factor (LRF; a measure of the light reflected from the object under a standard illuminant) depends on variation in key properties of naturalistic scenes. Specifically, we study how variation in target object reflectance, illumination spectra, and the reflectance of background objects in a scene impact estimation of a target object's LRF. To do this, we applied supervised statistical learning methods to the simulated excitations of human cone photoreceptors, obtained from labeled naturalistic images. The naturalistic images were rendered with computer graphics. The illumination spectra of the light sources and the reflectance spectra of the surfaces in the scene were generated using statistical models of natural spectral variation. Optimally decoding target object LRF from the responses of a small learned set of task-specific linear receptive fields that operate on a contrast representation of the cone excitations yields estimates that are within 13% of the correct LRF. Our work provides a framework for evaluating how different sources of scene variability limit performance on luminance constancy.

Color Perception: Objects, Constancy, and Categories

Color has been scientifically investigated by linking color appearance to colorimetric measurements of the light that enters the eye. However, the main purpose of color perception is not to determine the properties of incident light, but to aid the visual perception of objects and materials in our environment. We review the state of the art on object colors, color constancy, and color categories to gain insight into the functional aspects of color perception. The common ground between these areas of research is that color appearance is tightly linked to the identification of objects and materials and the communication across observers. In conclusion, we argue that research should focus on how color processing is adapted to the surface properties of objects in the natural environment in order to bridge the gap between the known early stages of color perception and the subjective appearance of color.

Processing of chromatic information in a deep convolutional neural network

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.