A Classification Method for the Dirty Factor of Banknotes Based on Neural Network with Sine Basis Functions

Author

He, Kexue ; Peng, Shurong ; Li, Shutao

Author_Institution

Coll. of Electr. & Inf. Eng., Changsha Univ. of Sci. & Technol., Changsha

Volume

1

fYear

2008

fDate

20-22 Oct. 2008

Firstpage

159

Lastpage

162

Abstract

The classification on the dirty factor of the new and used banknotes is an important function of the note sorter. This paper proposes a classification method based on neural network with sine basis functions. The gray level histogram of banknote image is used as the characteristic vector to train the neural network. The classification effect is satisfying by this method.

Keywords

bank data processing; image classification; image segmentation; learning (artificial intelligence); neural nets; probability; statistical analysis; vectors; banknote dirty factor; banknote image classification method; characteristic vector; gray level histogram; image segmentation; neural network training; probability distribution; sine basis function; Automation; Computer networks; Educational institutions; Feature extraction; Histograms; Intelligent networks; Neural networks; Neurons; Probability distribution; Sorting; banknotes; classification; dirty factor; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on

Conference_Location

Hunan

Print_ISBN

978-0-7695-3357-5

Type

conf

DOI

10.1109/ICICTA.2008.35

Filename

4659463