Paper
16 July 2019 Detecting alterations in historical violins with optical monitoring
Alireza Rezaei, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi, Sylvie Le Hégarat
Author Affiliations +
Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 1117210 (2019) https://doi.org/10.1117/12.2521702
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France
Abstract
Some historical musical instruments are still played today, and are prone to be affected by mechanical wear of superficial varnishes in direct contact with the musicians. In a previous work, an ad-hoc monitoring plan for historical violins, that involves the use multiple non-invasive analytic techniques, achieved good performance. However, the high number of techniques is a limitation if we want to perform frequent checks. In this work, our aim is to rely only on UV induced fluorescence images for performing a fast, preliminary check and then, if a possible alteration is detected, conduct spectroscopic analyses, which are more precise but also more time consuming. In this study, we explore the a-contrario framework in order to allow for the automatic detection of significant changes in the superficial varnishes. The difficulty of detecting the changes is represented by the need to define the significance of a change, in the absence of a ground truth provided by the expert. Tests performed on samples that simulate the effect of surface alteration during time show promising results.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alireza Rezaei, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi, and Sylvie Le Hégarat "Detecting alterations in historical violins with optical monitoring", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117210 (16 July 2019); https://doi.org/10.1117/12.2521702
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KEYWORDS
RGB color model

Image registration

Data modeling

Image processing

Statistical modeling

Ultraviolet radiation

Color difference

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