Abstract
The bills in circulation generate a large amount of fatigue bills every year, causing various types of problems, such as the paper jam in automatic tellers due to overwork and exhaustion. A highly advanced bill classification technique, which distinguishes whether a bill is a reusable bill specifying the level of fatigue, is greatly required in order to comb out these problematic bills. Therefore, a purpose of this paper is to suggest a classification method of fatigue bills based on K-means with bill image data. The effectiveness of this approach is verified by the bill discriminant experimentation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Teranisi, M., Omatu, S., Kosaka, T.: Classification of Bill Fatigue Levels by Feature-Selected Acoustic Energy Pattern Using Competitive Neural Network. In: International Joint Conference on Neural Networks 2000, vol. 6, pp. 249–252. IEEE Press, Los Alamitos (2000)
Kang, D.S., Miyagi, H., Omatu, S.: Neuro-Fuzzy Classification of The New and Used Bills Using Acoustic Data. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 2649–2654 (2000)
Teranisi, M., Omatu, S., Kosaka, T.: Three-Level Classification of Bill Fatigue by Band-Limited Energy Patterns. IEEJ Transactions on Electronics, Information and Systems 120-C, 11, 1602–1608 (2000) (in Japanese)
Oyama, K., Kang, D., Miyagi, H.: Classification of Fatigue Bills by a Fuzzy Learning Vector Quantization Method. In: Proceedings of the 2005 IEICE General Conference, pp. D-12–D-45. The Institute Electronics, Information and Communication Engineers, Osaka (2005) (in Japanese)
Uehara, R., Kang, D., Miyagi, H.: Classification of Fatigue Bill with Independent Component Analysis. Technical Report of IEICE, PRMU, 107(384), 71–75 (2007) (in Japanese)
Ishigaki, T., Higuchi, T.: Detection of Worn-out Banknote by Using Acoustic Signals ~Time-varying Spectrum Classification by Divergence based Kernel Machines~. Journal of SICE 44(5), 444–449 (2008) (in Japanese)
Motooki, T., Omatu, S., Yoshioka, M., Teranishi, M.: Noise Reduction of Acoustic Data of Bill under Noisy Environment Using Adaptive Digital Filter and Neural Network. IEEJ Transactions on Electronics, Information and Systems 129-C, 9, 1724–1729 (2009) (in Japanese)
Yamamoto, K., Murakami, S.: A Study on Image Segmentation by K-Means Algorithm. Technical Report of IEICE, PRMU, Vol. 103(514), 83–88 (2003) (in Japanese)
Miyaguni, S., Kang, D.: Classification of Fatigue Bills Using by feature quantity of Wrinkles. Record of 2007 Joint Conference of Electrical and Electronics Engineers, 234–234 (2007) (in Japanese)
Miyaguni, S., Kang, D., Miyagi, H.: Classification of Fatigue Bills Using by Creases Feature. In: Proc. of the 8th International Conference on Applications and Principles of Information Science (APIS 2009), pp. 263–266 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, D. et al. (2010). Classification of Fatigue Bills Based on K-Means by Using Creases Feature. In: de Leon F. de Carvalho, A.P., Rodríguez-González, S., De Paz Santana, J.F., Rodríguez, J.M.C. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-14883-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14882-8
Online ISBN: 978-3-642-14883-5
eBook Packages: EngineeringEngineering (R0)