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Sensors 2015, 15(9), 21016-21032; doi:10.3390/s150921016

Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor

1
Division of Electronics and Electrical Engineering, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
2
Kisan Electronics, Sungsoo 2-ga 3-dong, Sungdong-gu, Seoul 133-831, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 17 July 2015 / Revised: 16 August 2015 / Accepted: 24 August 2015 / Published: 27 August 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1879 KB, uploaded 27 August 2015]   |  

Abstract

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods. View Full-Text
Keywords: classification of banknote fitness; one-dimensional line image sensor of visible light; discrete wavelet transform; linear regression analysis; support vector machine classification of banknote fitness; one-dimensional line image sensor of visible light; discrete wavelet transform; linear regression analysis; support vector machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pham, T.D.; Park, Y.H.; Kwon, S.Y.; Nguyen, D.T.; Vokhidov, H.; Park, K.R.; Jeong, D.S.; Yoon, S. Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor. Sensors 2015, 15, 21016-21032.

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