Journal article
Authors list: Ennis, R; Schiller, F; Toscani, M; Gegenfurtner, KR
Publication year: 2018
Pages: B256-B266
Journal: Journal of the Optical Society of America A Optics, Image Science and Vision
Volume number: 35
Issue number: 4
ISSN: 1084-7529
eISSN: 1520-8532
Open access status: Bronze
DOI Link: https://doi.org/10.1364/JOSAA.35.00B256
Publisher: Optica Publishing Group
Abstract:
We have built a hyperspectral database of 42 fruits and vegetables. Both the outside (skin) and inside of the objects were imaged. We used a Specim VNIR HS-CL-30-V8E-OEM mirror-scanning hyperspectral camera and took pictures at a spatial resolution of similar to 57 px/deg by 800 pixels at a wavelength resolution of similar to 1.12 nm. A stable, broadband illuminant was used. Images and software are freely available on our webserver (http://www.allpsych.uni-giessen.de/GHIFVD; pronounced "gift"). We performed two kinds of analyses on these images. First, when comparing the insides and outsides of the objects, we observed that the insides were lighter than the skins, and that the hues of the insides and skins were significantly correlated (circular correlation = 0.638). Second, we compared the color distribution within each object to corresponding human color discrimination thresholds. We found a significant correlation (0.75) between the orientation of ellipses fit to the chromaticity distributions of our fruits and vegetables with the orientations of interpolated MacAdam discrimination ellipses. This indicates a close relationship between sensory processing and the characteristics of environmental objects. (c) 2018 Optical Society of America
Citation Styles
Harvard Citation style: Ennis, R., Schiller, F., Toscani, M. and Gegenfurtner, K. (2018) Hyperspectral database of fruits and vegetables, Journal of the Optical Society of America A Optics, Image Science and Vision, 35(4), pp. B256-B266. https://doi.org/10.1364/JOSAA.35.00B256
APA Citation style: Ennis, R., Schiller, F., Toscani, M., & Gegenfurtner, K. (2018). Hyperspectral database of fruits and vegetables. Journal of the Optical Society of America A Optics, Image Science and Vision. 35(4), B256-B266. https://doi.org/10.1364/JOSAA.35.00B256