Abstract
The Pulcinella diagnostic project here presented aims at testing a wide range of methodologies, technologies, and instrumentations against the imaging diagnostics of the various natural and artificial features present in the wooden physical model reproducing an archetypical historical artifact and its most common defects and in homogeneities. Due to its experimental purposes, it configures as an open diagnostic protocol – or a protocol of protocols – allowing a scalability to as many tools as wanted and, within this approach, being able to compare standard and innovative diagnostics methods related to historical wooden sculptures. The first round of diagnostic techniques includes Structure from Motion photogrammetry, Multispectral imaging, active Infrared Thermography and Terahertz imaging. The first experimental datasets and their preliminary results are analyzed against their effectiveness and failures in retrieving and characterizing standardized, specific, and known defects and features of the wooden mask investigated.
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Acknowledgements
The authors thanks Antonio Trogu, Luigi Noli, Matteo Baire and Alessandro Fanti (University of Cagliari, DICAAR and DIEE) for their fundamental help in acquisitions of photogrammetric, multispectral, and thermographic datasets.
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Piroddi, L. et al. (2022). The Pulcinella Diagnostic Project: Introduction to the Study of the Performances of Close-Range Diagnostics Targeted to a Wooden Physical Twin of a Carnival Historical Mask. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13382. Springer, Cham. https://doi.org/10.1007/978-3-031-10592-0_37
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