conference paper

Stone-by-Stone Segmentation for Monitoring Large Historical Monuments Using Deep Neural Networks

Title
Stone-by-Stone Segmentation for Monitoring Large Historical Monuments Using Deep Neural Networks
Creator(s)
Koubouratou, Idjaton
Desquesnes, Xavier
Treuillet, Sylvie
Brunetaud, Xavier
Date
2021
Is Part Of
Pattern Recognition. ICPR International Workshops and Challenges
In Series
Lecture Notes in Computer Science
Pages
235-248
Language
eng
Publisher
Springer International Publishing
Place Published
Cham
ISBN
978-3-030-68787-8
DOI
https://doi.org/10.1007/978-3-030-68787-8_17
Zotero BiDiAr
https://www.zotero.org/groups/5293298/bidiar/items/NE5IFPFA/item-list
Abstract
Monitoring and restoration of cultural heritage buildings require the definition of an accurate health record. A critical step is the labeling of the exhaustive constitutive elements of the building. Stone-by-stone segmentation is a major part. Traditionally it is done by visual inspection and manual drawing on a 2D orthomosaic. This is an increasingly complex, time-consuming and resource-intensive task.

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Sharing structured archaeological 3D data: open source tools for artificial intelligence applications and collaborative frameworks
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