Abstract
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 mages. 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.
Original language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Pages | 1728-1734 |
Number of pages | 7 |
DOIs | |
Publication status | Published - 2010 |
Event | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States Duration: 13 Jun 2010 → 18 Jun 2010 |
Conference
Conference | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 |
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Country/Territory | United States |
City | San Francisco, CA |
Period | 13/06/10 → 18/06/10 |