ROBUST AUTOMATIC SEGMENTATION OF ANCIENT COINS
Sebastian Zambanini, Martin Kampel
2009
Abstract
Nowadays, ancient coins are becoming subject to a very large illicit trade. Thus, the interest in reliable automatic coin recognition systems within cultural heritage and law enforcement institutions rises rapidly. Central component in the permanent identification and traceability of coins is the underlying image recognition technology. Prior to any analysis a coin image has to be segmented into two areas: the area depicting the coin and the area belonging to the background. In this paper, we focus on the segmentation task as a preprocessing step for any automated coin recognition system. The objective is a robust segmentation procedure for a large variety of coin image styles. We present a simple and fast method for coin segmentation, based on local entropy and gray value range. Results of the developed algorithm are shown for an image database of ancient coins and demonstrate the benefits of our approach.
References
- Bowyer, K. B. (2000). Validation of medical image analysis techniques. In Handbook of Medical Imaging, volume 2, pages 567-607. Press Monograph.
- Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603-619.
- Kapur, J. N., Sahoo, P. K., and Wong, A. K. C. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. CVGIP, 29:273-285.
- Nölle, M., Penz, H., Rubik, M., Mayer, K. J., Holländer, I., and Granec, R. (2003). Dagobert - a new coin recognition and sorting system. In Proc. of DICTA'03, pages 329-338.
- Reisert, M., Ronneberger, O., and Burkhardt, H. (2006). An efficient gradient based registration technique for coin recognition. In Proc. of the Muscle CIS Coin Competition, pages 19-31.
- Russ, J. C. (2006). The Image Processing Handbook. CRC Press, 5th edition.
- van der Maaten, L. J. and Poon, P. (2006). Coin-o-matic: A fast system for reliable coin classification. In Proc. of the Muscle CIS Coin Competition, pages 07-18.
- Yanowitz, S. and Bruckstein, A. (1989). A new method for image segmentation. CVGIP, 46(1):82-95.
- Zaharieva, M., Huber-Mörk, R., Nölle, M., and Kampel, M. (2007). On ancient coin classification. In Proc. of VAST'07, pages 55-62.
Paper Citation
in Harvard Style
Zambanini S. and Kampel M. (2009). ROBUST AUTOMATIC SEGMENTATION OF ANCIENT COINS . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 273-276. DOI: 10.5220/0001798302730276
in Bibtex Style
@conference{visapp09,
author={Sebastian Zambanini and Martin Kampel},
title={ROBUST AUTOMATIC SEGMENTATION OF ANCIENT COINS},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={273-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001798302730276},
isbn={978-989-8111-69-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - ROBUST AUTOMATIC SEGMENTATION OF ANCIENT COINS
SN - 978-989-8111-69-2
AU - Zambanini S.
AU - Kampel M.
PY - 2009
SP - 273
EP - 276
DO - 10.5220/0001798302730276