Learning Thermographic Models for Optimal Image Processing of Decorated Surfaces †

Abstract

The use of infrared thermography presents unique perspectives in imaging of artifacts to help interrogate their surface and subsurface characteristics, highlight deviations and detect contrast. This research capitalizes on active and passive thermal imagery along with advanced machine learning-based algorithms for pre- and post-processing of acquired scans. Such codes operate efficiently (compress data) to help link the observed temperature variations and the thermophysical parameters of targeted samples. One such processing modality is dictionary learning, which infers a “frame dictionary” to help represent the scans as linear combinations of a small set of features, thus training data to show a sparse representation. This technique (along factorization and component analysis-based methods) was used in current research on ancient polychrome marquetries aimed at detecting aging anomalies. The presented research is unique in terms of the targeted samples and the applied approaches and should provide specific guidance to similar domains.