@misc{10481/108785,
year = {2026},
month = {4},
url = {https://hdl.handle.net/10481/108785},
abstract = {Artists commonly use a relatively reduced palette of pigments and mix them in different proportions to increase the gamut of colors present on artworks. In this study, a complete workflow for pigment identification using spectral unmixing of reflectance spectra in the visible and near infrared is presented. The algorithm includes superpixel segmentation as pre-processing to reduce the number of spectra that are unmixed. Then, a pre-extracted set of relevant color instances from the painting is used to build an adaptive subset of candidate pigments from a reference palette, and pigment identification is achieved by superpixel voting within the reduced subsets corresponding to the automatically extracted endmembers presence maps. Two different moments in time of a Maternity of the 16th century (original and restored) and a modern replica of the same painting are used to showcase the performance of the algorithm, which is able to correctly identify 80 % of the pigments present from a reference library of 23 pigments, taking less than three minutes for processing around 7000 spectra.},
organization = {MICIU/AEI /10.13039/501100011033, PID2021-124446NB-100},
organization = {ERDF, EU and by the Ministry of Universities, Spain, FPU2020-05532},
publisher = {Elsevier},
keywords = {Spectral imaging},
keywords = {Cultural heritage},
keywords = {Pigment identification},
title = {Adaptive algorithm for pigment identification from unmixing spectral data: Case study with two versions of a XVI century painting},
doi = {10.1016/j.talanta.2025.129170},
author = {Valero Benito, Eva María and Martínez Domingo, Miguel Ángel and Blanc García, María Rosario and López Baldomero, Ana Belén and López Montes, Ana María and Martín, Domingo and Abad Muñoz, David and Vílchez Quero, José Luis},
}