Machine Vision and Applications

, Volume 22, Issue 6, pp 983–994 | Cite as

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

  • Reinhold Huber-Mörk
  • Sebastian Zambanini
  • Maia Zaharieva
  • Martin Kampel
Original Paper


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%.


Coin identification Ancient coins Shape matching Local features Fusion 


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  1. 1.
    Bay, H., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. In: Proceedings of European Conference on Computer Vision, LNCS, vol. 3951, pp. 404–417. Springer (2006)Google Scholar
  2. 2.
    Belongie S., Malik J., Puzicha J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)CrossRefGoogle Scholar
  3. 3.
    Cooley J.W., Tukey J.W.: An algorithm for the machine calculation of complex fourier series. Math. Comput. 19, 297–301 (1965)MathSciNetCrossRefzbMATHGoogle 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 Int. Conf. on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, pp. 403–412 (1996)Google Scholar
  5. 5.
    Elkins, N.T.: A survey of the material and intellectual consequences of trading in undocumented ancient coins: a case study on the north american trade. Frankfurter elektronische Rundschau zur Altertumskunde 7, 1–13 (2008).
  6. 6.
    Felzenszwalb, P., Schwartz, J.: Hierarchical matching of deformable shapes. In: Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  7. 7.
    Ferrari V., Tuytelaars T., Gool L.V.: Simultaneous object recognition and segmentation from single or multiple model views. Int. J. Comput. Vis. 67(2), 159–188 (2006)CrossRefGoogle Scholar
  8. 8.
    Fukumi M., Omatu S., Takeda F., Kosaka T.: Rotation-invariant neural pattern recognition system with application to coin recognition. IEEE Trans. Neural Netw. 3, 272–279 (1992)CrossRefGoogle Scholar
  9. 9.
    Fürst, M., Kronreif, G., Wögerer, C., Rubik, M., Holländer, I., Penz, H.: Development of a mechatronic device for high speed coin sorting. In: Proc. of IEEE Int. Conf. on Industrial Technology, vol. 1, pp. 185–189. Maribor, Slovenia (2003)Google Scholar
  10. 10.
    Giusti N., Sperduti A.: Theoretical and experimental analysis of a two-stage system for classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 893–904 (2002)CrossRefGoogle Scholar
  11. 11.
    Golfarelli M., Maio D., Maltoni D.: On the error-reject trade-off in biometric verification systems. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 786–796 (1997)CrossRefGoogle Scholar
  12. 12.
    Goodman, M.: Numismatic Photography. Zyrus Press (2008)Google Scholar
  13. 13.
    Hibari, E., Arikawa, J.: Coin discriminating apparatus. European Patent EP1077434 (2001)Google Scholar
  14. 14.
    Hoberman, G.: The Art of Coins and Their Photography. Lund Humphries (1982)Google Scholar
  15. 15.
    Hoßfeld M., Chu W., Adameck M., Eich M.: Fast fast 3D-vision system to classify metallic coins by their embossed topography. Electron. Lett. Comput. Vis. Image Anal. 5(4), 47–63 (2006)Google Scholar
  16. 16.
    Howgego, C.J.: The potential for image analysis in numismatics. In: Images and Artefacts of the Ancient World, pp. 109–113 (2005)Google Scholar
  17. 17.
    Huber R., Ramoser H., Mayer K., Penz H., Rubik M.: Classification of coins using an eigenspace approach. Pattern Recogn. Lett. 26(1), 61–75 (2005)CrossRefGoogle Scholar
  18. 18.
    Huber-Mörk, R., Zaharieva, M., Czedik-Eysenberg, H.: Numismatic object identification using fusion of shape and local descriptors. In: Proceedings of International Symposium on Visual Computing, pp. 368–379, Las Vegas, NV, USA (2008)Google Scholar
  19. 19.
    Jeannin, S., Bober, M.: Description of core experiments for mpeg-7 motion/shape. Technical Report ISO/IEC JTC 1/SC 29/WG 11 MPEG99/N2690 (1999)Google Scholar
  20. 20.
    Kampel M., Huber-Mörk R., Zaharieva M.: Image-based retrieval and identification of ancient coins. IEEE Intell. Syst. 24(2), 26–34 (2009)CrossRefGoogle Scholar
  21. 21.
    Keogh E., Wei L., Xi X., Vlachos M., Lee S.H., Protopapas P.: Supporting exact indexing of arbitrarily rotated shapes and periodic time series under euclidean and warping distance measures. VLDB J. 18(3), 611–630 (2009)CrossRefGoogle Scholar
  22. 22.
    Kittler J., Hatef M., Duin R., Matas J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  23. 23.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of International Joint Conference of Artificial Intelligence, pp. 1137–1145 (1995)Google Scholar
  24. 24.
    Latecki, L.J., Lakämper, R., Eckhardt, U.: Shape descriptors for non-rigid shapes with a single closed contour. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 424–429 (2000)Google Scholar
  25. 25.
    Lazebnik S., Schmid C., Ponce J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1265–1799 (2005)CrossRefGoogle Scholar
  26. 26.
    Lewis, J.: Fast normalized cross-correlation. In: Proceedings of Vision Interface, pp. 120–123 (1995)Google Scholar
  27. 27.
    Ling H., Okada K.: An efficient earth mover’s distance algorithm for robust histogram comparison. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 840–853 (2007)CrossRefGoogle Scholar
  28. 28.
    Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  29. 29.
    Loy, G., Eklundh, J.O.: Detecting symmetry and symmetric constellations of features. In: Proceedings of European Conference on Computer Vision, LNCS, vol. 3952, pp. 508–521. Springer (2006)Google Scholar
  30. 30.
    McNeill, G., Vijayakumar, S.: 2D shape classification and retrieval. In: International Joint Conference on Artificial Intelligence, pp. 1483–1488 (2005)Google Scholar
  31. 31.
    Mikolajczyk, K., Leibe, B., Schiele, B.: Local features for object class recognition. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 1792–1799 (2005)Google Scholar
  32. 32.
    Mikolajczyk K., Schmid C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  33. 33.
    Murillo, A.C., Guerrero, J.J., Sagüés, C.: SURF features for efficient robot localization with omnidirectional images. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3901–3907 (2007)Google Scholar
  34. 34.
    Neubarth, S., Gerrity, D., Waechter, M., Everhart, D., Phillips, A.: Coin discrimination apparatus. Canadian Patent CA2426293 (1998)Google Scholar
  35. 35.
    Nölle, M., Penz, H., Rubik, M., Mayer, K.J., Holländer, I., Granec, R.: Dagobert—a new coin recognition and sorting system. In: Proceedings of International Conference on Digital Image Computing—Techniques and Applications, pp. 329–338 (2003)Google Scholar
  36. 36.
    Onodera, A., Sugata, M.: Coin discrimination method and device. United States Patent US2002005329 (2002)Google Scholar
  37. 37.
    Reisert, M., Ronneberger, O., Burkhardt, H.: An efficient gradient based registration technique for coin recognition. In: Proceedings of Muscle CIS Coin Competition Workshop, pp. 19–31. Berlin, Germany (2006)Google Scholar
  38. 38.
    Ruisz J., Biber J., Loipetsberger M.: Quality evaluation in resistance spot welding by analysing the weld fingerprint on metal bands by computer vision. Int. J. Adv. Manuf. Tech. 33(9-10), 952–960 (2007)CrossRefGoogle Scholar
  39. 39.
    Saber E., Tekalp A.M.: Integration of color, edge, shape and texture features for automatic region-based image annotation and retrieval. Electron. Imaging 7, 684–700 (1998)CrossRefGoogle Scholar
  40. 40.
    Senator, T.: Multi-stage classification. In: Proceedings of International Conference on Data Mining (2005)Google Scholar
  41. 41.
    Shah, G., Pester, A., Stern, C.: Low power coin discrimination apparatus. Canadian Patent CA1336782 (1986)Google Scholar
  42. 42.
    Shi, X., Manduchi, R.: A study on Bayes feature fusion for image classification. In: Proceedings of Computer Vision and Pattern Recognition Workshop, pp. 95–103, Madison, WI, USA (2003)Google Scholar
  43. 43.
    Sivic J., Schaffalitzky F., Zisserman A.: Object level grouping for video shots. Int. J. Comput. Vis. 67(2), 189–210 (2006)CrossRefGoogle Scholar
  44. 44.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 2nd edn. PWS-an Imprint of Brooks and Cole Publishing (1998)Google Scholar
  45. 45.
    Tsuji, K., Takahashi, M.: Coin discriminating apparatus. European Patent EP0798669 (1997)Google Scholar
  46. 46.
    Tuytelaars T., Gool L.V.: Matching widely separated views based on affine invariant regions. Int. J. Comput. Vis. 59(1), 61–85 (2004)CrossRefGoogle Scholar
  47. 47.
    Tuytelaars, T., Mikolajczyk, K.: Local Invariant Feature Detectors: A Survey. Now Publishers (2008)Google Scholar
  48. 48.
    Van Der Maaten, L., Postma, E.: Towards automatic coin classification. In: Proceedings of Conference on Electronic Imaging and the Visual Arts, pp. 19–26, Vienna, Austria (2006)Google Scholar
  49. 49.
    Vassilas, N., Skourlas, C.: Content-based coin retrieval using invariant features and self-organizing maps. In: Proceedings of International Conference on Artificial Neural Network, pp. 113–122 (2006)Google Scholar
  50. 50.
    Veltkamp, R.C.: Shape matching: Similarity measures and algorithms. Technical Report UU-CS Ext. rep. 2001-03, Utrecht University: Information and Computing Sciences, Utrecht, The Netherlands (2001)Google Scholar
  51. 51.
    Zaharieva, M., Huber-Mörk, R., Nölle, M., Kampel, M.: On ancient coin classification. In: Proceedings of International Symposium on Virtual Reality, Archaeology and Cultural Heritage, pp. 55–62 (2007)Google Scholar
  52. 52.
    Zambanini, S., Kampel, M.: Robust automatic segmentation of ancient coins. In: Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP’09), vol. 2, pp. 273–276 (2009)Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Reinhold Huber-Mörk
    • 1
  • Sebastian Zambanini
    • 2
  • Maia Zaharieva
    • 3
  • Martin Kampel
    • 2
  1. 1.Business Unit High Performance Image Processing, Department Safety & SecurityAustrian Institute of Technology GmbHSeibersdorfAustria
  2. 2.Computer Vision Lab, Institute of Computer-Aided AutomationVienna University of TechnologyViennaAustria
  3. 3.Interactive Media Systems Group, Institute of Software Technology and Interactive SystemsVienna University of TechnologyViennaAustria

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