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Visual pattern recognition with neural networks

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Parallel Image Analysis (ICPIA 1992)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 654))

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Abstract

The “neocognitron” was first proposed as a hierarchical neural network model for the mechanism of visual pattern recognition in the brain. It is capable of deformation-resistant pattern recognition. Various experiments have demonstrated its powerful ability to recognize visual patterns. For example, the authors have designed several systems which recognize hand-written characters, such as, a system recognizing ten numerals, and a system recognizing alphanumeric characters. This paper also discusses recent advances in the neocognitron. The network has been modified to have an architecture closer to that of the real biological brain, and a new learning algorithm has been introduced.

The “selective attention model” has not only forward but also backward connections in a hierarchical network. It has the ability to segment patterns, as well as the function of recognizing them. The principles of this selective attention model can be extended to be used for several applications: for example, the recognition and segmentation of connected characters in cursive handwriting of English words, and the recognition of Chinese characters.

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References

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Author information

Authors and Affiliations

  1. Faculty of Engineering Science, Osaka University, Toyonaka, 560, Osaka, Japan

    Kunihiko Fukushima

Authors
  1. Kunihiko Fukushima
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Editor information

Akira Nakamura Maurice Nivat Ahmed Saoudi Patrick S. P. Wang Katsushi Inoue

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© 1992 Springer-Verlag Berlin Heidelberg

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Fukushima, K. (1992). Visual pattern recognition with neural networks. In: Nakamura, A., Nivat, M., Saoudi, A., Wang, P.S.P., Inoue, K. (eds) Parallel Image Analysis. ICPIA 1992. Lecture Notes in Computer Science, vol 654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56346-6_26

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  • DOI: https://doi.org/10.1007/3-540-56346-6_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56346-4

  • Online ISBN: 978-3-540-47538-5

  • eBook Packages: Springer Book Archive

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