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dc.contributor.advisorYan, Wei Qi
dc.contributor.authorRen, Yueqiu
dc.date.accessioned2017-11-03T02:47:44Z
dc.date.available2017-11-03T02:47:44Z
dc.date.copyright2017
dc.date.created2017
dc.identifier.urihttp://hdl.handle.net/10292/10941
dc.description.abstractFinancial institutions have adopted various automated banking systems using currency recognition as their main activity, which makes automated currency recognition of significant interest. However, after the review of the literature related to banknote recognition, it turns out that there has not been found any methods implemented or proposed for the recognition of the newly released banknotes. This thesis investigates various methods for achieving banknote real-time recognition using digital image processing. The new Series 7 New Zealand banknotes are considered as an example for intelligent banknote recognition in real time. Several experiments have been conducted in this study and two groups of training datasets are generated for comparison. One group is composed of banknote images produced by using scanners, and the other group is made up of banknote images captured by webcam. Various combinations of extracted features and classifiers have been analysed. The corresponding results are compared and the performance of each combined method is evaluated. Eventually, the PCA-based composite feature together with the BPNN is the combined method proposed in this thesis. The proposed method has demonstrated excellent performance and comparatively less time-consumption that makes it suitable for real-time applications. To the best of our knowledge, the composite feature containing both colour and texture elements, presented in this thesis has appeared in the field of banknote recognition for the first time. Our contribution is that this research project fills the vacancy of the real-time recognition of the newly released banknotes; and the proposed method paves the way for the future development of multi-currency real-time recognition.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectSeries 7 New Zealand paper currencyen_NZ
dc.subjectReal-time banknote recognitionen_NZ
dc.subjectUniform LBPen_NZ
dc.subjectBack-propagation neural networken_NZ
dc.subjectF-measureen_NZ
dc.titleBanknote Recognition in Real Time Using ANNen_NZ
dc.typeThesis
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2017-11-03T02:30:36Z


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