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3-Dimensional Object Recognition by Evolutional RBF Network

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2773))

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Abstract

This paper tries to recognize 3-dimensional objects by using an evolutional RBF network. Our proposed RBF network has the structure of preparing four RBFs for each hidden layer unit, selecting based on the Euclid distance between an input image and RBF. This structure can be invariant to 2- dimensional rotation by 90 degree. The other rotational invariance can be achieved by the RBF network. In hidden layer units, the number of RBFs, form, and arrangement are determined using real-coded GA. Computer simulations show object recognition can be done using such a method.

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References

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Author information

Authors and Affiliations

  1. Graduate School of Engineering, University of Tokushima, 2-1 Minami-josanjima, Tokushima, Japan

    Hideki Matsuda

  2. Faculty of Engineering, University of Tokushima, 2-1 Minami-josanjima, Tokushima, Japan

    Yasue Mitsukura, Minoru Fukumi & Norio Akamatsu

Authors
  1. Hideki Matsuda
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  2. Yasue Mitsukura
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  3. Minoru Fukumi
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  4. Norio Akamatsu
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, United Kingdom

    Vasile Palade

  2. Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, BN2 4GJ, Brighton, United Kingdom

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, 5095, Adelaide, SA, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Matsuda, H., Mitsukura, Y., Fukumi, M., Akamatsu, N. (2003). 3-Dimensional Object Recognition by Evolutional RBF Network. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_76

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  • DOI: https://doi.org/10.1007/978-3-540-45224-9_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40803-1

  • Online ISBN: 978-3-540-45224-9

  • eBook Packages: Springer Book Archive

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