Recognizing Ancient Coins Based on Local Features

  • Martin Kampel
  • Maia Zaharieva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)

Abstract

Numismatics deals with various historical aspects of the phenomenon money. Fundamental part of a numismatists work is the identification and classification of coins according to standard reference books. The recognition of ancient coins is a highly complex task that requires years of experience in the entire field of numismatics. To date, no optical recognition system for ancient coins has been investigated successfully. In this paper, we present an extension and combination of local image descriptors relevant for ancient coin recognition. Interest points are detected and their appearance is described by local descriptors. Coin recognition is based on the selection of similar images based on feature matching. Experiments are presented for a database containing ancient coin images demonstrating the feasibility of our approach.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Duncan-Jones, R.: Money and Government in the Roman Empire. Cambridge (1994)Google Scholar
  2. 2.
    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, 272–279 (1992)CrossRefGoogle Scholar
  3. 3.
    Mitsukura, Y., Fukumi, M., Akamatsu, N.: Design and evaluation of neural networks for coin recognition by using ga and sa. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, vol. 5, pp. 178–183 (2000)Google 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.
    Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  6. 6.
    Moreno, J.M., Madrenas, J., Cabestany, J., Laúna, J.R.: Using classical and evolutive neural models in industrial applications: A case study for an automatic coin classifier. In: Biological and Artificial Computation: From Neuroscience to Neurotechnology, pp. 922–931 (1997)Google Scholar
  7. 7.
    Bremananth, R., Balaji, B., Sankari, M., Chitra, A.: A new approach to coin recognition using neural pattern analysis. In: Proceedings of IEEE INDICON 2005, pp. 366–370 (2005)Google Scholar
  8. 8.
    Khashman, A., Sekeroglu, B., Dimililer, K.: Rotated Coin Recognition Using Neural Networks. Advances in Soft Computing, vol. 41, pp. 290–297. Springer, Berlin (2007)Google Scholar
  9. 9.
    Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M.: Classification of coins using an eigenspace approach. Pattern Recognition Letters 26, 61–75 (2005)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Nölle, M., Rubik, M., Hanbury, A.: Results of the muscle cis coin competition 2006. In: Proceedings of the Muscle CIS Coin Competition Workshop, Berlin, Germany, pp. 1–5 (2006)Google Scholar
  13. 13.
    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
  14. 14.
    Zaharieva, M., Kampel, M., Zambanini, S.: Image based recognition of ancient coins. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 547–554. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Murillo, A.C., Guerrero, J.J., Sagüés, C.: Surf features for efficient robot localization with omnidirectional images. In: IEEE International Conference on Robotics and Automation, pp. 3901–3907 (2007)Google Scholar
  17. 17.
    Loy, G., Eklundh, J.O.: Detecting symmetry and symmetric constellations of features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 508–521. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Tuytelaars, T., Van Gool, L.: Matching widely separated views based on affine invariant regions. International Journal of Computer Vision 59, 61–85 (2004)CrossRefGoogle Scholar
  19. 19.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65, 43–72 (2005)CrossRefGoogle Scholar
  20. 20.
    Schmid, C., Mohr, R., Baukhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 2, 151–172 (2000)CrossRefGoogle Scholar
  21. 21.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Conference, pp. 147–152 (1988)Google Scholar
  22. 22.
    Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)CrossRefGoogle Scholar
  23. 23.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  25. 25.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the Britisch Machine Vision Conference, London, vol. 1, pp. 384–393 (2002)Google Scholar
  26. 26.
    Lowe, D.G.: Object recognition from local schale-invariant features. In: International Conference on Computer Vision (ICCV 1999), Washington, DC, USA, vol. 2, pp. 1150–1157. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  27. 27.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)CrossRefGoogle Scholar
  28. 28.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 509–522 (2002)CrossRefGoogle Scholar
  29. 29.
    Stark, M., Schiele, B.: How good are local features for classes of geometric objects. In: 11th International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brazil (2007)Google Scholar
  30. 30.
    Zaharieva, M., Huber-Mörk, R., Nölle, M., Kampel, M.: On ancient coin classification. In: Arnold, D., Chalmers, A., Niccolucci, F. (eds.) 8th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST 2007), Eurograpchics, pp. 55–62 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Martin Kampel
    • 1
  • Maia Zaharieva
    • 1
  1. 1.Pattern Recognition and Image Processing GroupTU ViennaViennaAustria

Personalised recommendations