Hyperspectral database of fruits and vegetables

Ennis, Robert; Schiller, Florian; Toscani, Matteo; Gegenfurtner, Karl R.

doi:10.1364/josaa.35.00b256

Cited by 35 publications

(28 citation statements)

References 34 publications

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“…Hyperspectral images were taken under the same conditions as the photographs used in the experiments, although they appeared less sharp, probably because of the limitations of the optics of the hyperspectral camera. Our hyperspectral measuring system is described in detail in our previous work (Ennis, Schiller, Toscani, & Gegenfurtner, 2018). Analyses of the hyperspectral images confirmed the results from the photographs.…”

Section: Stimuli and Apparatussupporting

confidence: 63%

“…For example, when judging the lightness of a texture pattern, observers based their matches either on the lightest or the darkest areas, depending on contrast relative to the background ( Toscani, Valsecchi, & Gegenfurtner, 2013b ). Furthermore, although color matching experiments had shown that observers tend to base their matches on the most saturated parts of the targets’ color distributions, when participants are asked to classify a large sample of photographs of leaves they based their judgments on the average chromaticity of the leaves’ color distributions ( Milojevic, Ennis, Toscani, & Gegenfurtner, 2018 ). Our results provide additional evidence that color matching is based on different parts of the targets’ color distributions according to the task demands.…”

Section: Discussionmentioning

confidence: 99%

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Color consistency in the appearance of bleached fabrics

et al. 2020

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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.

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Section: Stimuli and Apparatussupporting

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Color consistency in the appearance of bleached fabrics

et al. 2020

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“…For example, there are additional datasets of measured surface reflectances that could be incorporated into future analyses. Some of these datasets focused on the reflectance of objects (e.g., fruit) that are thought to be important for the evolution of primate color vision (e.g., Sumner & Mollon, 2000; Regan et al, 2001; Barnard et al, 2002; Ennis, Schiller, Toscani, & Gegenfurtner, 2018). Another issue, not addressed by these datasets, is relative frequency of different surface reflectances in natural viewing.…”

Section: Discussionmentioning

confidence: 99%

Computational luminance constancy from naturalistic images

et al. 2018

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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.

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“…In case fresh fruit and vegetables, or controllable multiprimary LEDs, are unavailable, the neon fruit illusion may also be simulated. This would not be possible with standard three-channel photographs, but a good approximation can be achieved using a recently published hyperspectral database of fruits and vegetables (Ennis, Schiller, Toscani, & Gegenfurtner, 2018). The results of a lime and a red pepper, illuminated by a narrowband green LED with and without the minor contribution of a narrowband red LED, are shown in Figure 4.…”

mentioning

confidence: 99%

“…A simulation of the neon fruit illusion, using hyperspectral images of a lime and a red pepper from a database of hyperspectral images of fruits and vegetables (Ennis et al., 2018). We estimated reflectance of the lime and red pepper by dividing the spectrum of reflected light reported in the database by a measurement of the illuminant spectrum at a pixel location free from obvious specular highlights.…”

mentioning

confidence: 99%

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

2019

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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.

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Hyperspectral database of fruits and vegetables

Cited by 35 publications

References 34 publications

Color consistency in the appearance of bleached fabrics

Color consistency in the appearance of bleached fabrics

Computational luminance constancy from naturalistic images

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

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