DocumentCode :
1748971
Title :
Italian Lira classification by LVQ
Author :
Omatu, Sigeru ; Fujinaka, Toru ; Kosaka, Toshihisa ; Yanagimoto, Hidekazu ; Yoshioka, Michifumi
Author_Institution :
Osaka Prefecture Univ., Japan
Volume :
4
fYear :
2001
fDate :
2001
Firstpage :
2947
Abstract :
In this paper, a new method to classify the Italian Liras by using the learning vector quantization (LVQ) is proposed. The Italian Liras of 8 kinds, 1000, 2000, 5000, 10000, 50000 (new), 50000 (old), 100000 (new), 100000 (old) Liras with four directions A,B,C, and D are used, where A and B mean the normal direction and the upside down direction and C and D mean the reverse version of A and B. The original image with 128 by 64 pixels is observed at the transaction machine in which rotation and shift are included. After correction of these effects, we select a suitable area which shows the bill image and feed the image with 64 by 15 pixels to a neural network. Although the neural network of the LVQ type can process in any order of the dimension of the input data, the smaller size is better to achieve a faster convergence
Keywords :
bank data processing; convergence; image classification; image coding; neural nets; unsupervised learning; vector quantisation; Italian Lira; bank notes; convergence; image classification; learning vector quantization; neural network; transaction machine; Biological neural networks; Clustering algorithms; Convergence; Feeds; Humans; Neurons; Pattern matching; Pattern recognition; Pixel; Size measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.938846
Filename :
938846
Link To Document :
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