A Hierarchical Classification Method for US Bank-Notes

Tatsuhiko KAGEHIRO  Hiroto NAGAYOSHI  Hiroshi SAKO  

IEICE TRANSACTIONS on Information and Systems   Vol.E89-D   No.7   pp.2061-2067
Publication Date: 2006/07/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.7.2061
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)
Category: Pattern Discrimination and Classification
bank-note,  classification,  automatic setting,  error estimation,  

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This paper describes a method for the classification of bank-notes. The algorithm has three stages, and classifies bank-notes with very low error rates and at high speeds. To achieve the very low error rates, the result of classification is checked in the final stage by using different features to those used in the first two. High-speed processing is mainly achieved by the hierarchical structure, which leads to low computational costs. In evaluation on 32,850 samples of US bank-notes, with the same number used for training, the algorithm classified all samples precisely with no error sample. We estimate that the worst error rate is 3.1E-9 for the classification statistically.