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Identification of ancient coins based on fusion of shape and local features

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Abstract

We present a vision-based approach to ancient coins’ identification. The approach is a two-stage procedure. In the first stage an invariant shape description of the coin edge is computed and matching based on shape is performed. The second stage uses preselection by the first stage in order to refine the matching using local descriptors. Results for different descriptors and coin sides are combined using naive Bayesian fusion. Identification rates on a comprehensive data set of 2400 images of ancient coins are on the order of magnitude of 99%.

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Authors and Affiliations

  1. Business Unit High Performance Image Processing, Department Safety & Security, Austrian Institute of Technology GmbH, 2444, Seibersdorf, Austria

    Reinhold Huber-Mörk

  2. Computer Vision Lab, Institute of Computer-Aided Automation, Vienna University of Technology, Favoritenstr. 9/183-2, 1040, Vienna, Austria

    Sebastian Zambanini & Martin Kampel

  3. Interactive Media Systems Group, Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstr. 9-11/188/2, 1040, Vienna, Austria

    Maia Zaharieva

Authors
  1. Reinhold Huber-Mörk
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  2. Sebastian Zambanini
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  3. Maia Zaharieva
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  4. Martin Kampel
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Corresponding author

Correspondence to Reinhold Huber-Mörk.

Additional information

This work was partly supported by the European Union under grant FP6-SSP5-044450. However, this paper reflects only the authors’ views and the European Community is not liable for any use that may be made of the information contained herein.

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Huber-Mörk, R., Zambanini, S., Zaharieva, M. et al. Identification of ancient coins based on fusion of shape and local features. Machine Vision and Applications 22, 983–994 (2011). https://doi.org/10.1007/s00138-010-0283-y

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  • DOI: https://doi.org/10.1007/s00138-010-0283-y

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