• 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