Imaging Science Ph.D. Defense: Tania Kleynhans
Towards automatic pigment classification in painted works of art from diffuse reflectance image data
Imaging Science Ph.D. Candidate
Chester F. Carlson Center for Imaging Science, RIT
Information about artists' material used in paintings, obtained from the analysis of limited micro-samples, have assisted conservators to better define treatment plans, and provided scholars with basic information about the artists working methods. 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 yet been streamlined and currently requires significant manual input by experts. Additionally, HSI systems are often too expensive for small to mid-level museums to acquire. This research focused on three main objectives, namely 1) adapt 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 output pigment maps that can 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 multi spectral system that can be added to the scanning tables already part of many museum conservation laboratories. Algorithms developed for both the 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.
Undergraduates and graduates. Those with interest in the topic.
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Open to the Public