New feature extraction approach for bank note classification using Quaternion Wavelets

Authors:
Shan Gai;Guowei Yang;Sheng Zhang
Affiliations:
Department of Information Engineering, University of Nanchang Hangkong, Jiangxi Nanchang, P.R. China;Department of Information Engineering, University of Nanchang Hangkong, Jiangxi Nanchang, P.R. China;Department of Information Engineering, University of Nanchang Hangkong, Jiangxi Nanchang, P.R. China
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2013

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Abstract

In order to improve the performance of the banknote image classification, new feature extraction method based on quaternion wavelet transform QWT is proposed in this article. The QWT yields one shift-invariant magnitude and three phases by the quaternion algebra. The statistical characteristics such as mean, standard deviation and entropy are used as feature vector in the banknote image classification. The experimental results show that the proposed method by the QWT obtains better classification results than the conventional methods.