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dc.contributor.authorRitz, Martinen_US
dc.contributor.authorSantos, Pedroen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorPonchio, Federicoen_US
dc.contributor.editorPintus, Ruggeroen_US
dc.description.abstractManual classification of artefacts is a labor intensive process. Based on 2D images and 3D scans of - for example - ceramic shards, we developed a pattern recognition algorithm which automatically extracts relief features for each newly recorded object and tries to automate the classification process. Based on characteristics found, previously unknown objects are automatically corelated to already classified objects of a collection exhibiting the greatest similarity. As a result, classes of artefacts form iteratively, which ultimately also corresponds to the overall goal which is the automated classification of entire collections. The greatest challenge in developing our software approach was the heterogeneity of reliefs, and in particular the fact that current machine learning approaches were out of question due to the very limited number of objects per class. This led to the implementation of an analytical approach that is capable of performing a classification based on very few artefacts.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.subjectCCS Concepts: Computing methodologies --> Image processing; Applied computing --> Archaeology
dc.subjectComputing methodologies
dc.subjectImage processing
dc.subjectApplied computing
dc.titleAutomated Classification of Crests on Pottery Sherds Using Pattern Recognition on 2D Imagesen_US
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.description.sectionheadersSession 6
dc.identifier.pages4 pages

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License