Fast country classification of banknotes

Jiheon Ok, Chulhee Lee, Euisun Choi, Yoonkil Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we present a fast algorithm for country classification of banknotes. The algorithm can be used as an initial step for conventional banknote classification methods developed for a single currency in multi-country environment. We assume that the input image is a Contact Image Sensor (CIS) scan image with de-skewing and Region of Interest (ROI) extraction. In the training process, after size normalization we extract eigenimage for a banknote group based on overall context similarity. With the dominant eigenimage of each banknote group, we compute correlation metrics between the dominant eigenimage and test images. We tested the algorithm with four currencies: USD, KRW, CNY and EUR. The proposed method shows 100% accuracy and it took about 0.37ms for a banknote.

Original languageEnglish
Title of host publicationProceedings - 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013
Pages234-236
Number of pages3
DOIs
Publication statusPublished - 2013 May 20
Event4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013 - Bangkok, Thailand
Duration: 2013 Jan 292013 Jan 31

Other

Other4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013
CountryThailand
CityBangkok
Period13/1/2913/1/31

Fingerprint

Currency
Contact sensors
Image Sensor
Region of Interest
Image sensors
Fast Algorithm
Normalization
Contact
Metric
Similarity
Context
Training

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Modelling and Simulation
  • Theoretical Computer Science

Cite this

Ok, J., Lee, C., Choi, E., & Baek, Y. (2013). Fast country classification of banknotes. In Proceedings - 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013 (pp. 234-236). [6498271] https://doi.org/10.1109/ISMS.2013.34
Ok, Jiheon ; Lee, Chulhee ; Choi, Euisun ; Baek, Yoonkil. / Fast country classification of banknotes. Proceedings - 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013. 2013. pp. 234-236
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abstract = "In this paper, we present a fast algorithm for country classification of banknotes. The algorithm can be used as an initial step for conventional banknote classification methods developed for a single currency in multi-country environment. We assume that the input image is a Contact Image Sensor (CIS) scan image with de-skewing and Region of Interest (ROI) extraction. In the training process, after size normalization we extract eigenimage for a banknote group based on overall context similarity. With the dominant eigenimage of each banknote group, we compute correlation metrics between the dominant eigenimage and test images. We tested the algorithm with four currencies: USD, KRW, CNY and EUR. The proposed method shows 100{\%} accuracy and it took about 0.37ms for a banknote.",
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Ok, J, Lee, C, Choi, E & Baek, Y 2013, Fast country classification of banknotes. in Proceedings - 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013., 6498271, pp. 234-236, 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013, Bangkok, Thailand, 13/1/29. https://doi.org/10.1109/ISMS.2013.34

Fast country classification of banknotes. / Ok, Jiheon; Lee, Chulhee; Choi, Euisun; Baek, Yoonkil.

Proceedings - 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013. 2013. p. 234-236 6498271.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Ok J, Lee C, Choi E, Baek Y. Fast country classification of banknotes. In Proceedings - 4th International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2013. 2013. p. 234-236. 6498271 https://doi.org/10.1109/ISMS.2013.34