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Automatic attribution of ancient Roman imperial coins

Arandjelovic, Ognjen 2010, Automatic attribution of ancient Roman imperial coins, in CVPR 2010 : Proceedings of the Computer Vision and Pattern Recognition Conference 2010, IEEE, Piscataway, New Jersey, pp. 1728-1734, doi: 10.1109/CVPR.2010.5539841.

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Title Automatic attribution of ancient Roman imperial coins
Author(s) Arandjelovic, Ognjen
Conference name Computer Vison and Pattern Recognition. Conference (2010 : San Francisco, California)
Conference location San Francisco, California
Conference dates 13-18 June 2010
Title of proceedings CVPR 2010 : Proceedings of the Computer Vision and Pattern Recognition Conference 2010
Editor(s) [Unknown]
Publication date 2010
Conference series Computer Vision and Pattern Recognition Conference
Start page 1728
End page 1734
Total pages 7
Publisher IEEE
Place of publication Piscataway, New Jersey
Keyword(s) classification of coins
coin matching techniques
Summary Classification of coins is an important but laborious aspect of numismatics - the field that studies coins and currency. It is particularly challenging in the case of ancient coins. Due to the way they were manufactured, as well as wear from use and exposure to chemicals in the soil, the same ancient coin type can exhibit great variability in appearance. We demonstrate that geometry-free models of appearance do not perform better than chance on this task and that only a small improvement is gained by previously proposed models of combined appearance and geometry. Thus, our first major contribution is a new type of feature which is efficient in terms of computational time and storage requirements, and which effectively captures geometric configurations between descriptors corresponding to local features. Our second contribution is a description of a fully automatic system based on the proposed features, which robustly localizes, segments out and classifies coins from cluttered images. We also describe a large database of ancient coins that we collected and which will be made publicly available. Finally, we report the results of empirical comparison of different coin matching techniques. The features proposed in this paper are found to greatly outperform existing methods.
ISBN 9781424469840
Language eng
DOI 10.1109/CVPR.2010.5539841
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2010, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058425

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.