We extended and make publicly available the ARCA dataset, increasing the number of images from 328 to 1488, and the number of samples from 56,160 to 315,333. Moreover, we also added in the dataset the ground truth labels for all the images, and the sampled values, resulting in a dataset more valuable then before. Images in the dataset come in several versions, particularly the ``Original'' ones depict the Munsell Soils Color Charts together with a reference white, that could be useful for future works.
Professional tools like the ones recommended by the CIE are good, but at a cost. Once we consolidated through this work that neither transformation equations nor classifiers are able to generalize an automatic Munsell color specification model, we are planning to design a solution that could be based onto Computer Vision, in order to automatically recognize a reference marker (i.e., a White Calibration Plate CM-A145, or a Macbeth ColorChecker). Moreover, leveraging the synthetic images rendering procedure leveraged in this work, we open the path for the fast creation of a new extended dataset made of images with tremendously high variable illuminants. A dataset of this kind could be definitely employed in a future investigation based on deep learning techniques.
This paper is part of the project ARCA. Previously published papers and datasets can be found in the followings:
ARCA is a joint project between University of Catania and University of South Florida
Principal Investigators: F. Stanco and D. Tanasi
Development Supervisors: F.L.M. Milotta, D. Allegra and F. Stanco
Developers: G. Furnari and C. Quattrocchi
Colorimetry Experts: A.M. Gueli, S. Pasquale and G. Stella
==> Download from here the ARCA1488 Dataset. (2.81GB, Last Updated 13-JUL-19)