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Supporting Ancient Coin Classification by Image-Based Reverse Side Symbol Recognition

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 8048)

Abstract

Coins and currency are studied in the field of Numismatics. Our aim in this article is to use the knowledge of Numismatics for the development of part of a framework for the visual classification of ancient coins. Symbols minted on the reverse side of these coins vary greatly in their shapes and visual structures. Due to this property of symbols, we propose to use them as a discriminative feature for the visual classification of ancient coins. We use dense sampling based bag of visual words (BoVWs) approach for our problem. Due to the fact that BoVWs lack the spatial information, we evaluate three types of schemes to incorporate spatial information. Other parameters of BoVWs such as the size of visual vocabulary, level of detail of the dense sampling grid and number of features per image to construct the visual vocabulary are also investigated.

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Anwar, H., Zambanini, S., Kampel, M. (2013). Supporting Ancient Coin Classification by Image-Based Reverse Side Symbol Recognition. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_3

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  • DOI: https://doi.org/10.1007/978-3-642-40246-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40245-6

  • Online ISBN: 978-3-642-40246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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