@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00834344v1> a dcat:Dataset ;
    dct:description """
              In this thesis, we address two main problems namely the quantitative evaluation of mesh segmentation algorithms and learning mesh segmentation by exploiting the human factor. We propose the following contributions: - A benchmark dedicated to the evaluation of mesh segmentation algorithms. The benchmark includes a human-made ground-truth segmentation corpus and a relevant similarity metric that quantifies the consistency between these ground-truth segmentations and automatic ones produced by a given algorithm on the same models. Additionally, we conduct extensive experiments including subjective ones to respectively demonstrate and validate the relevance of our benchmark. - A new learning mesh segmentation algorithm. A boundary edge function is learned, using multiple geometric criteria, from a set of human segmented training meshes and then used, through a processing pipeline, to segment any input mesh. We show, through a set of experiments using different benchmarks, the performance superiority of our algorithm over the state-of-the-art. We present also an application of our segmentation algorithm for kinematic skeleton extraction of dynamic 3D-meshes.
            """ ;
    dct:identifier "tel-00834344" ;
    dct:issued "2026-05-10T17:13:01.045530"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-10T17:13:01.045535"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "3D-mesh segmentation: automatic evaluation and a new learning-based method" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00834344v1/resource/17187378-6de7-4abd-9256-0067a98a5cca> ;
    dcat:keyword "3d-mesh-segmentation",
        "apprentissage",
        "benchmark",
        "evaluation",
        "ground-truth",
        "infoeu-reposemanticsdoctoralthesis",
        "infoinfo-cvcomputer-science-cscomputer-vision-and-pattern-recognition-cscv",
        "learning",
        "segmentation-de-maillages-3d",
        "subjective-tests",
        "tests-subjectifs",
        "theses",
        "verite-terrain" ;
    dcat:landingPage <https://theses.hal.science/tel-00834344> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00834344v1/resource/17187378-6de7-4abd-9256-0067a98a5cca> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-10T17:13:01.049626"^^xsd:dateTime ;
    dct:modified "2026-05-10T17:13:01.032691"^^xsd:dateTime ;
    dct:title "3D-mesh segmentation: automatic evaluation and a new learning-based method" ;
    dcat:accessURL <https://theses.hal.science/tel-00834344> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://theses.hal.science/tel-00834344> a foaf:Document .