Cooper, N. J., Gorban, A., Allison, P. M., Tyukin, I., Sofeikov, K. and Levesley, J. (2018). Exploring Automated Pottery Identification [Arch-I-Scan]. Internet Archaeology 50: Big Data on the Roman Table: new approaches to tablewares in the Roman world. Vol 50, https://doi.org/10.11141/ia.50.11.
 Title The title of the publication or report |
Exploring Automated Pottery Identification [Arch-I-Scan] | ||||||
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Issue The name of the volume or issue |
Internet Archaeology 50: Big Data on the Roman Table: new approaches to tablewares in the Roman world | ||||||
Series The series the publication or report is included in |
Internet Archaeology | ||||||
Volume Volume number and part |
50 | ||||||
Biblio Note This is a Bibliographic record only. |
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Licence Type ADS, CC-BY 4.0 or CC-BY 4.0 NC. |
Creative Commons Attribution 3.0
International Licence |
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Publication Type The type of publication - report, monograph, journal article or chapter from a book |
Journal | ||||||
Abstract The abstract describing the content of the publication or report |
A hand-held smart device technology (Arch-I-Scan) is currently being developed and tested for scanning and classifying archaeological artefacts. The technology is based on a new platform developed by ARM, jointly with University of Leicester within Innovate UK Knowledge Transfer Partnership project (code KTP009890), and takes advantage of new algorithms for one-trial learning based on measure concentration phenomenon in high dimensions. This article discusses the development of a 'proof of concept' for automating the classification of Roman ceramic vessel types using whole vessels held in the collections of the Jewry Wall Museum, Leicester. The 'proof of concept' illustrates the viability and technical possibility of classifying and discriminating between objects of different types on-the-fly from a limited number of images. This technology is based on recent results (Gorban et al. 2016; Gorban and Tyukin 2017) revealing peculiar geometric properties of finite but large samples of data in high dimension. The ambition is to create a dedicated software that turns commonly available devices such as smart phones or tablets into scanners capable of classifying even small vessel sherds to the correct form and fabric. | ||||||
Year of Publication The year the book, article or report was published |
2018 | ||||||
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Locations Any locations covered by the publication or report. This is not the place the book or report was published. |
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Source Where the record has come from or which dataset it was orginally included in. |
ADS Library
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Created Date The date the record of the pubication was first entered |
01 Apr 2019 |
