A Reliable Classification Method for Paper Currency Based on the Non-Linear PCA
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概要
- 論文の詳細を見る
This paper addresses the reliability of neuro-classifiers for paper currency recognition. A local principal component analysis (PCA) method is applied to remove non-linear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map (SOM) clustering and then the PCA is performed on each region. A learning vector quantization (LVQ) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. The experimental results taken from 1, 200 samples of US dollar bills show that the reliability is increased up to 100% when the number of regions as well as number of codebook vectors in the LVQ classifier are taken properly.
- 社団法人 電気学会の論文
- 2003-10-01
著者
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Ahmadi Ali
Department Of Computer And System Science Osaka Prefecture University
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Omatu S
Division Of Computer And Systems Sciences Graduate School Of Engineering Osaka Prefecture University
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Omatu Sigeru
Department Of Computer And System Sciences College Of Engineering Osaka Prefecture University
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KOSAKA Toshihisa
Glory Ltd.
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