{
    "o:id": 15451,
    "url": "https://chloe.cnr.it/s/BiDiAr/item/15451",
    "o:resource_template": "Conference Paper",
    "o:resource_class": "fabio:ConferencePaper",
    "dcterms:title": [
        "Stone-by-Stone Segmentation for Monitoring Large Historical Monuments Using Deep Neural Networks"
    ],
    "dcterms:creator": [
        "Koubouratou, Idjaton",
        "Desquesnes, Xavier",
        "Treuillet, Sylvie",
        "Brunetaud, Xavier"
    ],
    "dcterms:publisher": [
        "Springer International Publishing"
    ],
    "dcterms:date": [
        "2021"
    ],
    "dcterms:language": [
        "eng"
    ],
    "dcterms: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."
    ],
    "dcterms:isPartOf": [
        "Pattern Recognition. ICPR International Workshops and Challenges"
    ],
    "dcterms:spatial": [
        "Cham"
    ],
    "bibo:doi": [
        "https://doi.org/10.1007/978-3-030-68787-8_17"
    ],
    "bibo:isbn": [
        "978-3-030-68787-8"
    ],
    "bibo:pages": [
        "235-248"
    ],
    "foaf:homepage": [
        "https://www.zotero.org/groups/5293298/bidiar/items/NE5IFPFA/item-list"
    ],
    "dcat:inSeries": [
        "Lecture Notes in Computer Science"
    ]
},