Image Based Recognition of Ancient Coins

  • Maia Zaharieva
  • Martin Kampel
  • Sebastian Zambanini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)


Illegal trade and theft of coins appears to be a major part of the illegal antiques market. Image based recognition of coins could substantially contribute to fight against it. Central component in the permanent identification and traceability of coins is the underlying classification and identification technology. However, currently available algorithms focus basically on the recognition of modern coins. To date, no optical recognition system for ancient coins has been researched successfully. In this paper, we give an overview on recent research for coin classification and we show if existing approaches can be extended from modern coins to ancient coins. Results of the algorithms implemented are presented for three different coins databases with more then 10.000 coins.


Sift Feature Segmentation Error Illicit Trade Generalize Hough Transform Ancient Coin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Fukumi, M., Omatu, S., Takeda, F., Kosaka, T.: Rotation-invariant neural pattern recognition system with application to coin recognition. IEEE Transactions on Neural Networks 3(2), 272–279 (1992)CrossRefGoogle Scholar
  2. 2.
    Bremananth, R., Balaji, B., Sankari, M., Chitra, A.: A new approach to coin recognition using neural pattern analysis. In: Proc. of IEEE Indicon 2005 Conference, pp. 366–370 (2005)Google Scholar
  3. 3.
    Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M.: Classification of coins using an eigenspace approach. Pattern Recognition Letters 26(1), 61–75 (2005)CrossRefGoogle Scholar
  4. 4.
    Davidsson, P.: Coin classification using a novel technique for learning characteristic decision trees by controlling the degree of generalization. In: IEA/AIE-96. Proc. of 9th Int. Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, pp. 403–412 (1996)Google Scholar
  5. 5.
    Nölle, M., Penz, H., Rubik, M., Mayer, K.J., Holländer, I., Granec, R.: Dagobert – a new coin recognition and sorting system. In: DICTA 2003. Proc. of the 7th International Conference on Digital Image Computing - Techniques and Applications, Macquarie University, Sydney, Australia, pp. 329–338, CSIRO Publishing (2003)Google Scholar
  6. 6.
    Reisert, M., Ronneberger, O., Burkhardt, H.: An efficient gradient based registration technique for coin recognition. In: Proc. of the Muscle CIS Coin Competition Workshop, Berlin, Germany, pp. 19–31 (2006)Google Scholar
  7. 7.
    van der Maaten, L.J., Postma, E.O.: Towards automatic coin classification. In: Proc. of the EVA-Vienna 2006, Vienna, Austria, pp. 19–26 (2006)Google Scholar
  8. 8.
    van der Maaten, L.J., Poon, P.: Coin-o-matic: A fast system for reliable coin classification. In: Proc. of the Muscle CIS Coin Competition Workshop, Berlin, Germany, pp. 7–18 (2006)Google Scholar
  9. 9.
    Nölle, M., Rubik, M., Hanbury, A.: Results of the muscle cis coin competition 2006. In: Proc. of the Muscle CIS Coin Competition Workshop, Berlin, Germany, pp. 1–5 (2006)Google Scholar
  10. 10.
    Russ, J.C.: The Image Processing Handbook, 2nd edn. CRC Press, Boca Raton, FL (1995)Google Scholar
  11. 11.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Maia Zaharieva
    • 1
  • Martin Kampel
    • 1
  • Sebastian Zambanini
    • 1
  1. 1.Vienna University of Technology, Institute of Computer Aided Automation, Pattern Recognition and Image Processing Group, Favoritenstr. 9/1832, A-1040 ViennaAustria

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