Laser-Aided Profile Measurement and Cluster Analysis of Ceramic Shapes.
Ceramics are one of the commonest sources of archaeological information, yet their abundance often confounds documentation and analysis. This article presents a new method of documenting and analyzing ceramics that includes laser-aided profile measurement to capture ceramic shape and other informati...
| Publicado en: | Journal of Field Archaeology Vol. 48; no. 1; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=160848558&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 160848558 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00934690 3VS jtl: Journal of Field Archaeology issn: 00934690 maglogo: N pubinfo: dt: Feb2023 vid: 48 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 160848558 10.1080/00934690.2022.2128549 ppf: 1 ppct: 18 formats: tig: atl: Laser-Aided Profile Measurement and Cluster Analysis of Ceramic Shapes. aug: au: Demján, Peter Pavúk, Peter Roosevelt, Christopher H. affil: Institute of Archaeology of the Czech Academy of Sciences, Prague, Czech Republic Charles University, Prague, Czech Republic Koç University, Istanbul, Turkey su: Cluster analysis (Statistics) Ceramics Database management software Bronze Age Machine learning Point cloud Türkiye sug: subj: Türkiye Cluster analysis (Statistics) Ceramics Database management software Bronze Age Machine learning Point cloud keyword: automated shape matching computational ceramic classification digital recording Kaymakçı unsupervised machine-learning western Anatolia ab: Ceramics are one of the commonest sources of archaeological information, yet their abundance often confounds documentation and analysis. This article presents a new method of documenting and analyzing ceramics that includes laser-aided profile measurement to capture ceramic shape and other information quickly and accurately, resulting in digital outputs suitable for both publication and morphometric analysis. Linked software and database solutions enable unsupervised machine learning to cluster shapes based on similarity, eventually assisting typological analysis. Following an overview of current practices in ceramic recording and both standard and computational shape classification analyses, the new approach is discussed in full as a documentary and analytical tool. A case study from the Middle and Late Bronze Age site of Kaymakçı in western Anatolia demonstrates the benefits of the recording method and helps show that a combination of automated and manual shape clustering techniques currently remains the best practice in ceramic shape classification. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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