MillefioriAnalyzer: machine learning, computer vision and visual analytics for provenance research of Ancient Roman artefacts

Wiebel, A., Gloger, O. and Eckardt, H. ORCID: https://orcid.org/0000-0001-9288-5624 (2025) MillefioriAnalyzer: machine learning, computer vision and visual analytics for provenance research of Ancient Roman artefacts. In: 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 26-28 Feb 2025, Porto, Portugal, pp. 807-814. doi: 10.5220/0013098900003912 (ISBN: 9789897587283)

Abstract/Summary

In this position paper, we explore ways to digitally support provenance research of ancient Roman artefacts decorated with millefiori. In particular, we discuss experiments applying visual analytics, computer vision and machine learning approaches to analyze the relations between images of individual millefiori slices called florets. We start by applying automatic image analysis approaches to the florets and discover that image quality and the small overall number of images pose serious challenges to these approaches. To address these challenges, we bring human intuition and pattern recognition abilities back into the analysis loop by developing and employing visual analytics techniques. We achieve a convenient analysis workflow for the archaeologists by integrating all approaches into a single interactive software tool which we call MillefioriAnalyzer. The software is tailored to fit the needs of the archaeological application case and links the automatic image analysis approaches with the interactive visual analytics views. As appropriate for a research software, MillefioriAnalyzer is open-source and publicly available. First results include an automatic approximate ordering of florets and a visual analytics module improving upon the current manual image layout for further analytic reasoning.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/120108
Identification Number/DOI	10.5220/0013098900003912
Refereed	Yes
Divisions	Science > School of Archaeology, Geography and Environmental Science > Department of Archaeology
Publisher	IVAPP
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