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Data from: Differentiating Biological Colours with Few and Many Sensors: Spectral Reconstruction with RGB and Hyperspectral Cameras

RMIT University, Australia
Assoc Professor Adrian Dyer (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29370&rft.title=Data from: Differentiating Biological Colours with Few and Many Sensors: Spectral Reconstruction with RGB and Hyperspectral Cameras&rft.identifier=5b9de831d1368324eca970df36993d06&rft.publisher=RMIT University, Australia&rft.description=This Dataverse holds supporting data for the paper Differentiating biological colour with few and many sensors: Spectral reconstruction with RGB and hyperspectral cameras by Garcia, Girard, Kasumovic, Petersen, Wilksch and Dyer 2015. The data consists on: a) non-linear RGB TIFF images; b) linearised version of images in a) stored as 3888 x 2592 x 3 matlab matrices where monochrome images corresponding to the red, green and blue colour channels are stored along the thrid dimension of the matrix, following Matlab's standard format for RGB images; c) A Matlab files directory containing a matrix (.mat) file storing linear RGB values for the calibration set and reflectance spectra readings corresponding to each sample in the set. This data is required for the spectral reconstruction of any given RGB combination. Code for performing the spectral reconstruction mentioned in the paper is also provided as a plain, txt file. Copy and paste the contents of this file into a matlab function page for creating the function in your own machine. d) An excel file containing the data in c) for reference purposes. The authors provide the code without any warranty. Please refer to the publication for more details.&rft.creator=Assoc Professor Adrian Dyer&rft.date=2017&rft.relation=https://doi.org/10.1371/journal.pone.0125817&rft_rights=All rights reserved.&rft_rights=CC BY-NC: Attribution-Noncommercial 3.0 AU http://creativecommons.org/licenses/by-nc/3.0/au&rft_subject=Article&rft_subject=Optical resolution&rft_subject=Photoreceptor&rft_subject=RGB camera&rft_subject=Spectral sensitivity&rft_subject=Camera&rft_subject=Color discrimination&rft_subject=Color vision&rft_subject=Controlled study &rft_subject=Hyperspectral camera&rft_subject=Image processing &rft_subject=Image reconstruction&rft_subject=Imaging system&rft_subject=NANOTECHNOLOGY&rft_subject=TECHNOLOGY&rft.type=dataset&rft.language=English Access the data
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This Dataverse holds supporting data for the paper "Differentiating biological colour with few and many sensors: Spectral reconstruction with RGB and hyperspectral cameras" by Garcia, Girard, Kasumovic, Petersen, Wilksch and Dyer 2015. The data consists on: a) non-linear RGB TIFF images; b) linearised version of images in a) stored as 3888 x 2592 x 3 matlab matrices where monochrome images corresponding to the red, green and blue colour channels are stored along the thrid dimension of the matrix, following Matlab's standard format for RGB images; c) A Matlab files directory containing a matrix (.mat) file storing linear RGB values for the calibration set and reflectance spectra readings corresponding to each sample in the set. This data is required for the spectral reconstruction of any given RGB combination. Code for performing the spectral reconstruction mentioned in the paper is also provided as a plain, txt file. Copy and paste the contents of this file into a matlab function page for creating the function in your own machine. d) An excel file containing the data in c) for reference purposes. The authors provide the code without any warranty. Please refer to the publication for more details.

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Article | Camera | Color discrimination | Color vision | Controlled study | Hyperspectral camera | Image processing | Image reconstruction | Imaging system | Nanotechnology | Optical resolution | Photoreceptor | RGB camera | Spectral sensitivity | Technology |

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