Hyperspectral imaging in color vision research: tutorial

Foster, David H.; Amano, Kinjiro

doi:10.1364/josaa.36.000606

Cited by 60 publications

(71 citation statements)

References 133 publications

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“…The reflectance spectra of the Portuguese paintings and the natural scenes were acquired using a spatial resolution of 1024 × 1344 pixels. The acquisition procedure and the accuracy of the methodology was described elsewhere [3,30], but the accuracy of the syst The reflectance spectra of the Japanese paintings were acquired using a spatial resolution of 1024 × 1932 pixels (Nuance-VIS, Cambridge Research and Instrumentation, Inc., , Hopkinton, MA, USA). The reduced bandwidth from 400 to 420 of these images, when compared to the other paintings from Portugal and Italy, are not expected to impact the overall result.…”

Section: Paintings and Natural Scenesmentioning

confidence: 99%

Section: Camera and Standard Observer Spectral Sensitivitymentioning

confidence: 99%

“…The CIELAB color space was used as a starting point because it is a perceptual system [19], although with some limitations regarding uniformity [29]. To estimate the chromaticity coordinates of each image pixel the tristimulus values were converted into L * (the lightness of the color, 0 for black and 100 for white), a * (with negative values representing green and positive values representing red) and b * (with negative values representing blue and positive values representing yellow) assuming the CIE D65 and the LED illuminants, as described elsewhere [3,19]. The CIELAB color space has an associated color difference formula (∆E * ab = (∆L * ) 2 + (∆a * ) 2 + (∆b * ) 2 ), based on the Euclidean distance between two chromaticity coordinates [18] by comparing the difference for each color attribute.…”

Section: Color Differencesmentioning

confidence: 99%

“…Thus, cameras produce colors that are an approximation of what we see when looking at those scenarios. On the other hand, hyperspectral images acquire the spatial information without the chromatic compression found in digital cameras, maintaining the spectral properties of the light signal, information relevant to research in vision [3].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?—A Study Based on Hyperspectral Imaging

Linhares

Monteiro

Bailão

et al. 2020

Sensors

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RGB digital cameras (RGB) compress the spectral information into a trichromatic system capable of approximately representing the actual colors of objects. Although RGB digital cameras follow the same compression philosophy as the human eye (OBS), the spectral sensitivity is different. To what extent they provide the same chromatic experiences is still an open question, especially with complex images. We addressed this question by comparing the actual colors derived from spectral imaging with those obtained with RGB cameras. The data from hyperspectral imaging of 50 natural scenes and 89 paintings was used to estimate the chromatic differences between OBS and RGB. The corresponding color errors were estimated and analyzed in the color spaces CIELAB (using the color difference formulas ΔE*ab and CIEDE2000), Jzazbz, and iCAM06. In CIELAB the most frequent error (using ΔE*ab) found was 5 for both paintings and natural scenes, a similarity that held for the other spaces tested. In addition, the distribution of errors across the color space shows that the errors are small in the achromatic region and increase with saturation. Overall, the results indicate that the chromatic errors estimated are close to the acceptance error and therefore RGB digital cameras are able to produce quite realistic colors of complex scenarios.

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Section: Paintings and Natural Scenesmentioning

confidence: 99%

Section: Camera and Standard Observer Spectral Sensitivitymentioning

confidence: 99%

Section: Color Differencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?—A Study Based on Hyperspectral Imaging

Linhares

Monteiro

Bailão

et al. 2020

Sensors

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show abstract

“…Finally, tenth, (x) four sphere images were stitched together based on the SIFT automatic feature detection algorithm [19] to produce the four-pi steradian full panorama environmental illumination map. Parts of the methods described here are analogous to those described in a recent tutorial paper [20]. Figure 3 shows the sRGB representation of the collected hyperspectral environmental illumination maps for eight outdoor scenes and four indoor scenes (panels (a) and (b) respectively).…”

Section: Image Processingmentioning

confidence: 99%

Hyperspectral environmental illumination maps: Characterizing directional spectral variation in natural environments

Morimoto

Kishigami

Linhares

et al. 2019

Preprint

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Objects placed in real-world scenes receive incident light from every direction, and the spectral content of this light may vary from one direction to another. In computer graphics, environmental illumination is approximated using maps that specify illumination at a point as a function of incident angle. However, to-date, existing public databases of environmental illumination maps specify only three colour channels (RGB). We have captured a new set of 12 environmental illumination maps (eight outdoor scenes; four indoor scenes) using a hyperspectral imaging system with 33 spectral channels. The data reveal a striking directional variation of spectral distribution of lighting in natural environments. We discuss limitations of using daylight models to describe natural environmental illumination.

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Iridescence from Total Internal Reflection at 3D Microscale Interfaces: Mechanistic Insights and Spectral Analysis

et al. 2023

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An experimental investigation and the optical modeling of the structural coloration produced from total internal reflection interference within 3D microstructures are described. Ray‐tracing simulations coupled with color visualization and spectral analysis techniques are used to model, examine, and rationalize the iridescence generated for a range of microgeometries, including hemicylinders and truncated hemispheres, under varying illumination conditions. An approach to deconstruct the observed iridescence and complex far‐field spectral features into its elementary components and systematically link them to ray trajectories that emanate from the illuminated microstructures is demonstrated. The results are compared with experiments, wherein microstructures are fabricated with methods such as chemical etching, multiphoton lithography, and grayscale lithography. Microstructure arrays patterned on surfaces with varying orientation and size lead to unique color‐traveling optical effects and highlight opportunities for how total internal reflection interference can be used to create customizable reflective iridescence. The findings herein provide a robust conceptual framework for rationalizing this multibounce interference mechanism and establish approaches for characterizing and tailoring the optical and iridescent properties of microstructured surfaces.

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Hyperspectral imaging in color vision research: tutorial

Cited by 60 publications

References 133 publications

How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?—A Study Based on Hyperspectral Imaging

How Good Are RGB Cameras Retrieving Colors of Natural Scenes and Paintings?—A Study Based on Hyperspectral Imaging

Hyperspectral environmental illumination maps: Characterizing directional spectral variation in natural environments

Iridescence from Total Internal Reflection at 3D Microscale Interfaces: Mechanistic Insights and Spectral Analysis

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