Abstract: In this paper we propose a reliable surface imaging method for the non-invasive detection of morphological changes in paintings. Usually, the evaluation and quantification of changes and defects results mostly from an optical and subjective assessment, through the comparison of the previous and subsequent state of conservation and by means of condition reports. Using quantitative Reflectance Transformation Imaging (RTI) we obtain detailed information on the geometry and morphology of the painting surface with a fast, precise and non-invasive method. Accurate and quantitative measurements of deterioration were acquired after the painting experienced artificial damage. Morphological changes were documented using normal vector images while the intensity map succeeded in highlighting, quantifying and describing the physical changes. We estimate that the technique can detect a morphological damage slightly smaller than 0.3 mm, which would be difficult to detect with the eye, considering the painting size. This non-invasive tool could be very useful, for example, to examine paintings and artwork before they travel on loan or during a restoration. The method lends itself to automated analysis of large images and datasets. Quantitative RTI thus eases the transition of extending human vision into the realm of measuring change over time.
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Manfredi, M.; Bearman, G.; Williamson, G.; Kronkright, D.; Doehne, E.; Jacobs, M.; Marengo, E. A New Quantitative Method for the Non-Invasive Documentation of Morphological Damage in Paintings Using RTI Surface Normals. Sensors 2014, 14, 12271-12284.
Manfredi M, Bearman G, Williamson G, Kronkright D, Doehne E, Jacobs M, Marengo E. A New Quantitative Method for the Non-Invasive Documentation of Morphological Damage in Paintings Using RTI Surface Normals. Sensors. 2014; 14(7):12271-12284.
Manfredi, Marcello; Bearman, Greg; Williamson, Greg; Kronkright, Dale; Doehne, Eric; Jacobs, Megan; Marengo, Emilio. 2014. "A New Quantitative Method for the Non-Invasive Documentation of Morphological Damage in Paintings Using RTI Surface Normals." Sensors 14, no. 7: 12271-12284.