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Localization of Attended Multi-feature Stimuli: Tracing Back Feed-Forward Activation Using Localized Saliency Computations

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Book cover Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

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Abstract

This paper demonstrates how attended stimuli may be localized even if they are complex items composed of elements from several different feature maps and from different locations within the Selective Tuning (ST) model. As such, this provides a step towards the solution of the ‘binding problem’ in vision. The solution relies on a region-based winner-take-all algorithm, a definition of a featural receptive field for neurons where several representations provide input from different spatial areas, and a localized, distributed saliency computation specialized for each featural receptive field depending on its inputs. A top-down attentive mechanism traces back the connections activated by feed-forward stimuli to localize and bind features into coherent wholes.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science & Engineering, and, Centre for Vision Research, York University, Toronto, Ontario, Canada

    John K. Tsotsos

Authors
  1. John K. Tsotsos
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Tsotsos, J.K. (2006). Localization of Attended Multi-feature Stimuli: Tracing Back Feed-Forward Activation Using Localized Saliency Computations. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_49

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  • DOI: https://doi.org/10.1007/11840930_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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