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Applied Intelligent Systems pp 301–320Cite as

Selective Attention Adaptive Resonance Theory and Object Recognition

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 153))

Abstract

The concept of selective attention as a useful mechanism in Artificial Neural Network models of visual pattern recognition has received a lot of attention recently, particularly since it was found that such a mechanism influences the receptive field profiles of cells in the primate visual pathway by filtering out non-relevant stimuli [28, 29]. It is believed that the massive feedback pathways in the brain play a role in attentional mechanisms by biasing the competition amongst the neural populations that are activated by different parts of a scene [8, 14, 15].

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Author information

Authors and Affiliations

  1. Weapons Systems Division, Defence Science and Technology Organisation, PO Box 1500, Edinburgh, SA, 5111, Australia

    Peter Lozo

  2. Tenix Defence Systems Pty Ltd, Second Avenue, Technology Park, Mawson Lakes, SA, 5095, Australia

    Jason Westmacott

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

    Quoc V. Do, Lakhmi C. Jain & Lai Wu

Authors
  1. Peter Lozo
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  2. Jason Westmacott
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  3. Quoc V. Do
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  4. Lakhmi C. Jain
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  5. Lai Wu
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Editor information

Editors and Affiliations

  1. School of Information, Technology & Computer Science, University of Wollongong, 2522, Wollongong, NSW, Australia

    John Fulcher

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

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Lozo, P., Westmacott, J., Do, Q.V., Jain, L.C., Wu, L. (2004). Selective Attention Adaptive Resonance Theory and Object Recognition. In: Fulcher, J., Jain, L.C. (eds) Applied Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39972-8_10

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  • DOI: https://doi.org/10.1007/978-3-540-39972-8_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05942-1

  • Online ISBN: 978-3-540-39972-8

  • eBook Packages: Springer Book Archive

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