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Numismatic Object Identification Using Fusion of Shape and Local Descriptors

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Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

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Abstract

Reliable object identification is an essential task in the process of recognition and traceability of stolen cultural heritage. Existing approaches for object recognition focus mainly on object classification. However, they are not sufficient to identify a given object among hundreds of objects of the same class. In this paper, we investigate the feasibility of computer aided identification of ancient coins. Since the shape of a coin is a very unique feature, we first apply a shape descriptor to capture its characteristics. In the next step, local features are used to describe the die information. We present experiments on a data set of 2400 images of ancient coins. The evaluation results show that our approach is competitive. Moreover, it indicates some outstanding features that show great promise for reliable object identification in the area of cultural heritage.

This work was partly supported by the European Union under grant FP6-SSP5-044450. However, this paper reflects only the authors’ views and the EC is not liable for any use that may be made of the information contained herein.

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Author information

Authors and Affiliations

  1. Austrian Research Centers GmbH - ARC, smart systems Division, Business unit High Performance Image Processing, A-2444, Seibersdorf, Austria

    R. Huber-Mörk & H. Czedik-Eysenberg

  2. Institute for Computer Aided Automation, Pattern Recognition & Image Processing Group, Vienna Univ. of Technology, A-1040, Vienna, Austria

    M. Zaharieva

Authors
  1. R. Huber-Mörk
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  2. M. Zaharieva
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  3. H. Czedik-Eysenberg
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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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Huber-Mörk, R., Zaharieva, M., Czedik-Eysenberg, H. (2008). Numismatic Object Identification Using Fusion of Shape and Local Descriptors . In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_36

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  • DOI: https://doi.org/10.1007/978-3-540-89646-3_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

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