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A Study on Hierarchical Recognition Algorithm of Multinational Banknotes Using SIFT Features
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 Title & Authors
A Study on Hierarchical Recognition Algorithm of Multinational Banknotes Using SIFT Features
Lee, Wang-Heon;
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 Abstract
In this paper, we not only take advantage of the SIFT features in banknote recognition, which has robustness to illumination changes, geometric rotation as well as scale changes, but also propose the hierarchical banknote recognition algorithm, which comprised of feature vector extraction from the frame grabbed image of the banknotes, and matching to the prepared data base of multinational banknotes by ANN algorithm. The images of banknote under the developed UV, IR and white illumination are used so as to extract the SIFT features peculiar to each banknotes. These SIFT features are used in recognition of the nationality as well as face value. We confirmed successful function of the proposed algorithm by applying the proposed algorithm to the banknotes of Korean and USD as well as EURO.
 Keywords
Banknote Recognition;Approximated Nearest Neighbour;RANSAC;Hierarchical Recognition;
 Language
Korean
 Cited by
 References
1.
J. Hara and H Miyahara, "Automtic banknote transaction apparatus," United States Patent, no. 5021639, 4 june, 1991.

2.
B. Storey, "Apparatus and method for testing banknotes for genuineness using Fourier transform analysis," United Sstaes Patent, no. 5530772, 21 October, 1998

3.
R. Cavalcanti and N Wallace, "A model of private bank-note issue," Review of Economic Dynamics, Elsevier, vol. 2, issue 1, Jan. 1999, pp. 104-136. crossref(new window)

4.
M. Aoba, T. Kikuchi, and Y. Takefuji, "Euro Banknote Recognition System Using a Three-layered Perceptron and RBF Networks," Information Processing Society of Japan, Trans. Mathematical Modelling and Its Applications, vol. 44, no. SIG 7(TOM 8), May 2003.

5.
D. Mount, "ANN Programming Manual," Dept. of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, 2005.

6.
David G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, 2004, pp. 91-110. crossref(new window)

7.
S. Se, David G. Lowe, and J. Little, "Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks," Int. J. of Robotics Research, vol. 21, no. 8, 2002, pp. 735-758. crossref(new window)

8.
S. Se, David G. Lowe, and Jim Little, "Global localization using distinctive visual features,-" Int. Conf. on Intelligent Robots and System pattern matching using wavelet decomposition," Pattern Recognition Letters, vol. 23, 2002, pp. 191-201. crossref(new window)

9.
H. Lam and C. Ng, "The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm," Engineering Structures, ELSEVIER, vol. 30, issue 10, Oct. 2008, pp. 2762-2770. crossref(new window)

10.
B. Kim, E. Lee, B. Suhng, D. Ryu, and W. Lee, "Feature Extraction using FFT for Banknotes Recognition in a Variety of Lighting Conditions," Int. Conf. on Control, Automation and Systems 2013, Gwangju, Korea, Oct. 2013, pp. 698-700.

11.
H. Lim, H. Yoon, C. Kim, and K.Lee, "F-Hessian SIFT-Based Railroad Level-Crossing Vision System," J. of the Korea Institute of Electronic Communication Sciences, vol.5 no.2, 2010, pp138-144

12.
J. Zhu, J. Chong, and K. kim, "The Recognition and Distance Estimation of a Golf Ball using a WebCam," J. of the Korea Institute of Electronic Communication Sciences, vol.8 no.12, 2013, pp1833-1839 crossref(new window)

13.
S. Kim, J. Song, and J. Park, "Height Estimation of pedestrian based on image," J. of the Korea Institute of Electronic Communication Sciences, vol.9 no.9, 2014, pp.1035-1042. crossref(new window)