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Recognizing Ancient Coins Based on Local Features

  • Conference paper
Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5358))

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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.

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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References

  1. Duncan-Jones, R.: Money and Government in the Roman Empire. Cambridge (1994)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)

    Article  Google Scholar 

  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. 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. Khashman, A., Sekeroglu, B., Dimililer, K.: Rotated Coin Recognition Using Neural Networks. Advances in Soft Computing, vol. 41, pp. 290–297. Springer, Berlin (2007)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Chapter  Google Scholar 

  15. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  18. Tuytelaars, T., Van Gool, L.: Matching widely separated views based on affine invariant regions. International Journal of Computer Vision 59, 61–85 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Schmid, C., Mohr, R., Baukhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 2, 151–172 (2000)

    Article  MATH  Google Scholar 

  21. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Conference, pp. 147–152 (1988)

    Google Scholar 

  22. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  24. Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)

    Article  Google Scholar 

  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. 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. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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 

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Author information

Authors and Affiliations

  1. Pattern Recognition and Image Processing Group, TU Vienna, Favoritenstr 9 - 11, 1040, Vienna, Austria

    Martin Kampel & Maia Zaharieva

Authors
  1. Martin Kampel
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  2. Maia Zaharieva
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Kampel, M., Zaharieva, M. (2008). Recognizing Ancient Coins Based on Local Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_2

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  • DOI: https://doi.org/10.1007/978-3-540-89639-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89638-8

  • Online ISBN: 978-3-540-89639-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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