Automatic Segmentation of Archaeological Fragments with Relief Patterns using Convolutional Neural Networks

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dc.contributor.authorThompson, Elia Moscosoen_US
dc.contributor.authorRanieri, Andreaen_US
dc.contributor.authorBiasotti, Silviaen_US
dc.contributor.editorHulusic, Vedad and Chalmers, Alanen_US
dc.date.accessioned2021-11-02T08:55:48Z
dc.date.available2021-11-02T08:55:48Z
dc.date.issued2021
dc.description.abstractThe recent commodification of high-quality 3D scanners is leading to the possibility of capturing models of archaeological finds and automatically recognizing their surface reliefs. We present our advancements in this field using Convolutional Neural Networks (CNNs) to segment and classify the region around a vertex in a robust way. The network is trained with high-resolution views of the 3D models captured at different angles. The views represent both the model with its original textures and a colorization of the patches according to the value of the Shape Index (SI) in their vertices. The SI encodes local surface variations and we exploit the colorization of the model driven by the SI to generate other view and enrich the dataset. Our method has been validated on a relief recognition benchmark on archaeological fragments proposed within the SHape REtrieval Contest (SHREC) 2018.en_US
dc.description.sectionheadersReconstruction
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20211411
dc.identifier.isbn978-3-03868-141-0
dc.identifier.issn2312-6124
dc.identifier.pages93-102
dc.identifier.urihttps://doi.org/10.2312/gch.20211411
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20211411
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer systems organization
dc.subjectNeural networks
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.titleAutomatic Segmentation of Archaeological Fragments with Relief Patterns using Convolutional Neural Networksen_US
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