This study is an ideal continuation of the one presented at REAACH-ID 2021. The results therein obtained are in fact the starting point for new evaluations and for the development of a protocol for the reconstruction of the missing parts in the Byzantine frescoes of St. Nicholas.The research in question aims to carry out, thanks to Artificial Intelligence, digital restorations useful both for the formal and symbolic analysis of Byzantine iconography and for its communication to a wide audience. Four phases describe the research strategy: 1) choice of the case study and the field of investigation; 2) identification of the formal parameters in the processing of the paintings: canon; 3) definition of the work-flow relating to the work of artificial intelligence; 4) application of the study to a specific case and analysis of the obtained results.

St. Nicholas of Myra. Reconstruction of the face between Canon and AI / Arena, Marinella; Lax, Gianluca. - (2022), pp. 62-72. [10.3280/oa-845-c196]

St. Nicholas of Myra. Reconstruction of the face between Canon and AI.

Arena, Marinella
;
Lax, Gianluca
2022-01-01

Abstract

This study is an ideal continuation of the one presented at REAACH-ID 2021. The results therein obtained are in fact the starting point for new evaluations and for the development of a protocol for the reconstruction of the missing parts in the Byzantine frescoes of St. Nicholas.The research in question aims to carry out, thanks to Artificial Intelligence, digital restorations useful both for the formal and symbolic analysis of Byzantine iconography and for its communication to a wide audience. Four phases describe the research strategy: 1) choice of the case study and the field of investigation; 2) identification of the formal parameters in the processing of the paintings: canon; 3) definition of the work-flow relating to the work of artificial intelligence; 4) application of the study to a specific case and analysis of the obtained results.
2022
9788835141945
byzantine frescoes, effigies, inpainting, pyton, torch
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/132466
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