Information about artists' materials used in paintings, obtained from the analysis of limited micro-samples, has assisted conservators to better define treatment plans, and provided scholars with basic information about the working methods of the artists. Recently, macro-scale imaging systems such as visible-to-near infrared (VNIR) reflectance hyperspectral imaging (HSI) are being used to provide conservators and art historians with a more comprehensive understanding of a given work of art. However, the HSI analysis process has not been streamlined and currently requires significant manual input by experts. Additionally, HSI systems are often too expensive for small to mid-level museums. This research focused on three main objectives: 1) adapt existing algorithms developed for remote sensing applications to automatically create classification and abundance maps to significantly reduce the time to analyze a given artwork, 2) create an end-to-end pigment identification convolutional neural network to produce pigment maps that may be used directly by conservation scientists without further analysis, and 3) propose and model the expected performance of a low-cost fiber optic single point multispectral system that may be added to the scanning tables already part of many museum conservation laboratories.
Algorithms developed for both classification and pigment maps were tested on HSI data collected from various illuminated manuscripts. Results demonstrate the potential of both developed processes. Band selection studies indicates that pigment identification from a small number of bands produces similar results to that of the HSI data sets on a selected number of test artifacts. A system level analysis of the proposed system was conducted with a detailed radiometric model. The system trade study confirmed the viability of using either individual spectral filters or a linear variable filter set-up to collect multispectral data for pigment identification of works of art.
Library of Congress Subject Headings
Automatic classification; Artists' materials--Classification; Painting--Imaging; Hyperspectral imaging; Reflectance; Neural networks (Computer science)
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Roger L. Easton Jr.
Kleynhans, Tania, "Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data" (2020). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus