Application of forensic photogrammetry and 3D modelling to improve epigraphic reading: study of the Roman altar of Gravesano (Ticino, Switzerland)

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Version: Final published version
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Download: Gravesano3D.pdf (5734.58 [Ko])
State: Public
Version: Supplementary document
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Download: GravesanoSetup.pdf (3025.48 [Ko])
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Version: Supplementary document
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Download: GravesanoVDO.mp4 (13748.38 [Ko])
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Version: Supplementary document
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Serval ID
serval:BIB_211A9E6B3BA6
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Application of forensic photogrammetry and 3D modelling to improve epigraphic reading: study of the Roman altar of Gravesano (Ticino, Switzerland)
Journal
The Journal of Epigraphic Studies
Author(s)
Dell'Era Romeo, Milliet Quentin, Montani Isabelle, Sapin Éric G.
ISSN
2611-979X
Publication state
Published
Issued date
2019
Peer-reviewed
Oui
Volume
2
Pages
113-124
Language
english
Abstract
The inscription on the Roman altar of Gravesano (Ticino, Switzerland) is complete, but its reading is arduous because of the eroded surface and the natural texture of the stone. A first reading was established with traditional epigraphic techniques (autopsy and paper squeeze), but the text remained partially undeciphered. In order to enhance the readings of stone inscriptions, a more extensive and accurate technique was deemed necessary. This involved a collaboration between specialists in Roman epigraphy and in forensic science. We propose the use of forensic photogrammetry, which has proven to be useful in the study of archaeological objects. The high-quality photographs and the resulting 3D dynamic model were examined and the reading of the inscription could be confirmed and improved. The details of that procedure are exposed and illustrated in this paper. A link points to the PDF 3D file obtained from the original 3D dynamic model.
Keywords
Roman Epigraphy, Forensic Science, Forensic Imaging, Forensic Photogrammetry, 3D Modelling
Create date
26/10/2019 15:38
Last modification date
06/09/2020 5:23
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