From Manual to Automated Optical Recognition of Ancient Coins

  • Maia Zaharieva
  • Martin Kampel
  • Klaus Vondrovec
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4820)

Abstract

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 over the challenges faced by optical recognition algorithms. Furthermore, we show that image based recognition can assist the manual process of coin classification and identification by restricting the range of possible coins of interest.

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References

  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: Proc. of 9th Int. Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-1996), 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: Proc. of the 7th International Conference on Digital Image Computing - Techniques and Applications (DICTA 2003), 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., 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
  8. 8.
    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
  9. 9.
    Göbl, R.: Antike Numismatik, München (1978)Google Scholar
  10. 10.
    Göbl, R.: Numismatik – Grundriß und wissenschaftliches System, München (1987)Google Scholar
  11. 11.
    Heath, M., Sarkar, S., Sanocki, T., Bowyer, K.: Comparison of edge detectors. Computer Vision and Image Understanding (1), 38–54 (1998)Google Scholar
  12. 12.
    Liu, J., Yang, Y.H.: Multiresolution color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 689–700 (1994)CrossRefGoogle Scholar
  13. 13.
    Shafarenko, L., Petrou, H., Kittler, J.: Histogram-based segmentation in a perceptually uniform color space. IEEE Transactions on Image Processing 7(9), 1354–1358 (1998)CrossRefGoogle Scholar
  14. 14.
    Hojjatoleslami, S., Kittler, J.: Region growing: A new approach. IEEE Transaction on Image Processing 7(7), 1079–1984 (1998)CrossRefGoogle Scholar
  15. 15.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)MATHCrossRefGoogle Scholar
  16. 16.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  17. 17.
    Zhang, G.P.: Neural networks for classification: A survey. IEEE Transactions on Systems, Man and Cybernetics 30(4), 451–462 (2000)CrossRefGoogle Scholar
  18. 18.
    Zaharieva, M., Kampel, M., Zambanini, S.: Image based recognition of ancient coins. In: Proc. of the 12th International Conference on Computer Analysis of Images and Patterns (CAIP), pp. 547–554 (2007)Google Scholar
  19. 19.
    Duncan-Jones, R.: Money and Government in the Roman Empire, Cambridge (1994)Google Scholar
  20. 20.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Maia Zaharieva
    • 1
  • Martin Kampel
    • 1
  • Klaus Vondrovec
    • 2
  1. 1.Vienna University of Technology Pattern Recognition and Image ProcessingViennaAustria
  2. 2.Museum of Fine Arts Department of Coin and MedalsViennaAustria maia

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