Aging Prediction of Cultural Heritage Samples Based on Surface Microgeometry

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dc.contributor.authorCiortan, Irina Mihaelaen_US
dc.contributor.authorMarchioro, Giacomoen_US
dc.contributor.authorDaffara, Claudiaen_US
dc.contributor.authorPintus, Ruggeroen_US
dc.contributor.authorGobbetti, Enricoen_US
dc.contributor.authorGiachetti, Andreaen_US
dc.contributor.editorSablatnig, Robert and Wimmer, Michaelen_US
dc.date.accessioned2018-11-11T10:57:32Z
dc.date.available2018-11-11T10:57:32Z
dc.date.issued2018
dc.description.abstractA critical and challenging aspect for the study of Cultural Heritage (CH) assets is related to the characterization of the materials that compose them and to the variation of these materials with time. In this paper, we exploit a realistic dataset of artificially aged metallic samples treated with different coatings commonly used for artworks' protection in order to evaluate different approaches to extract material features from high-resolution depth maps. In particular, we estimated, on microprofilometric surface acquisitions of the samples, performed at different aging steps, standard roughness descriptors used in materials science as well as classical and recent image texture descriptors. We analyzed the ability of the features to discriminate different aging steps and performed supervised classification tests showing the feasibility of a texture-based aging analysis and the effectiveness of coatings in reducing the surfaces' change with time.en_US
dc.description.sectionheadersDigital Documentation for Conservation
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20181352
dc.identifier.isbn978-3-03868-057-4
dc.identifier.issn2312-6124
dc.identifier.pages147-154
dc.identifier.urihttps://doi.org/10.2312/gch.20181352
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20181352
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectMachine learning approaches
dc.subjectNeural networks
dc.subjectApplied computing
dc.subjectArts and humanities
dc.subjectGeneral and reference
dc.subjectMetrics
dc.titleAging Prediction of Cultural Heritage Samples Based on Surface Microgeometryen_US
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GCH 2018 - Eurographics Workshop on Graphics and Cultural Heritage