Abstract
Illegal trade and theft of coins appears to be a major part of the illegal antiques market. Image based recognition of coins could substantially contribute to fight against it. Central component in the permanent identification and traceability of coins is the underlying classification and identification technology. However, currently available algorithms focus basically on the recognition of modern coins. To date, no optical recognition system for ancient coins has been researched successfully. In this paper, we give an overview over the challenges faced by optical recognition algorithms. Furthermore, we show that image based recognition can assist the manual process of coin classification and identification by restricting the range of possible coins of interest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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(2), 272–279 (1992)
Bremananth, R., Balaji, B., Sankari, M., Chitra, A.: A new approach to coin recognition using neural pattern analysis. In: Proc. of IEEE Indicon 2005 Conference, pp. 366–370 (2005)
Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M.: Classification of coins using an eigenspace approach. Pattern Recognition Letters 26(1), 61–75 (2005)
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)
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)
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)
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)
van der Maaten, L.J., Postma, E.O.: Towards automatic coin classification. In: Proc. of the EVA-Vienna 2006, Vienna, Austria, pp. 19–26 (2006)
Göbl, R.: Antike Numismatik, München (1978)
Göbl, R.: Numismatik – Grundriß und wissenschaftliches System, München (1987)
Heath, M., Sarkar, S., Sanocki, T., Bowyer, K.: Comparison of edge detectors. Computer Vision and Image Understanding (1), 38–54 (1998)
Liu, J., Yang, Y.H.: Multiresolution color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 689–700 (1994)
Shafarenko, L., Petrou, H., Kittler, J.: Histogram-based segmentation in a perceptually uniform color space. IEEE Transactions on Image Processing 7(9), 1354–1358 (1998)
Hojjatoleslami, S., Kittler, J.: Region growing: A new approach. IEEE Transaction on Image Processing 7(7), 1079–1984 (1998)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Zhang, G.P.: Neural networks for classification: A survey. IEEE Transactions on Systems, Man and Cybernetics 30(4), 451–462 (2000)
Zaharieva, M., Kampel, M., Zambanini, S.: Image based recognition of ancient coins. In: Proc. of the 12th International Conference on Computer Analysis of Images and Patterns (CAIP), pp. 547–554 (2007)
Duncan-Jones, R.: Money and Government in the Roman Empire, Cambridge (1994)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zaharieva, M., Kampel, M., Vondrovec, K. (2008). From Manual to Automated Optical Recognition of Ancient Coins. In: Wyeld, T.G., Kenderdine, S., Docherty, M. (eds) Virtual Systems and Multimedia. VSMM 2007. Lecture Notes in Computer Science, vol 4820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78566-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-78566-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78565-1
Online ISBN: 978-3-540-78566-8
eBook Packages: Computer ScienceComputer Science (R0)