Abstract
In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote’s color is characterized by summing all the color vectors of the image’s pixels to obtain a resultant vector, the banknote’s denomination is classified by knowing the orientation of the resulting vector within the RGB space. In order to obtain a more precise characterization of paper currency, the less discriminative colors of each denomination are eliminated from the images; the color selection is applied in the RGB and HSV spaces, separately. Experimental results with the current Mexican banknotes are presented.
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References
Bank for International Settlements: Monetary and Economic Department, “Triennial central bank survey for foreign exchange and derivatives market activity in December 2010: Preliminary global results” (2010)
Tecnocont, http://www.tecnocont.es
Galantz, http://www.galantz.com.ar
Jae-Kang, L., Il-Hwan, L.: New recognition algorithm for various kinds of Euro banknotes. In: Conf. of the IEEE Industrial Electronics Society (IECON), pp. 2266–2270 (2003)
Lee, J.K., Jeon, S.G., Kim, I.H.: Distinctive Point Extraction and Recognition Algorithm for Various Kinds of Euro Banknotes. Int. J. Control Autom. Syst. 2(2), 201–206 (2004)
Hasanuzzaman, F., Yang, X., Tian, Y.: Robust and effective component-based banknote recognition for the blind. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 42(6), 1021–1030 (2012)
Kagehiro, T., Nagayoshi, H., Sako, H.: A Hierarchical Classification Method for US Bank Notes. Trans. Inf. Syst. E89D(7), 2061–2067 (2006)
Sajal, R., Kamruzzaman, M., Jewel, F.: A machine vision based automatic system for real time recognition and sorting of Bangladesh bank notes. In: Int. Conf. Computer and Information Technology (ICCIT), pp. 560–567 (2008)
Poorrahangaryan, F., Mohammadpour, T., Kianisarkaleh, A.: A Persian banknote recognition using wavelet and neural network. In: Int. Conf. Computer Science and Electronics Engineering (ICCSEE), vol. 3, pp. 679–684 (2012)
Guo, J., Zhao, Y., Cai, A.: A reliable method for paper currency recognition based on LBP. In: IEEE Int. Conf. Network Infrastructure and Digital Content, pp. 359–363 (2010)
Hassanpour, H., Farahabadi, P.M.: Using Hidden Markov Models for Paper Currency Recognition. Expert Syst. Appl. 36(6), 10105–10111 (2009)
Takeda, F., Sakoobunthu, L., Satou, H.: Thai banknote recognition using neural network and continues learning by DSP unit. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS (LNAI), vol. 2773, pp. 1169–1177. Springer, Heidelberg (2003)
Paschos, G.: Perceptually Uniform Color Spaces for Color Texture Analysis: An Empirical Evaluation. IEEE Trans. Image Process 10(6), 932–937 (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall (2002)
Wang, F., Man, L., Wang, B., Xiao, Y., Pan, W., Lu, X.: Fuzzy-based Algorithm for Color Recognition of License Plates. Pattern Recognit. Lett. 29(7), 1007–1020 (2008)
Bronshtein, I., Semendyayev, K., Musiol, G., Muehlig, H.: Handbook of Mathematics. Springer, Heidelberg (2007)
Hagan, H.: Neural Network Design. PWS Publishing Company (1996)
García-Lamont, F., Cervantes, J., López, A.: Recognition of Mexican banknotes via their color and texture features. Expert Syst. Appl. 39(10), 9651–9660 (2012)
Lee, K.H., Park, T.H.: Image segmentation of UV for automatic paper-money inspection. In: Int. Conf. Control, Automation, Robotics and Vision, pp. 1175–1180 (2010)
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García-Lamont, F., Cervantes, J., López, A., Rodríguez, L. (2013). Classification of Mexican Paper Currency Denomination by Extracting Their Discriminative Colors. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_35
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DOI: https://doi.org/10.1007/978-3-642-45111-9_35
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