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Neural Mechanisms for Representing Surface and Contour Features

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Emergent Neural Computational Architectures Based on Neuroscience

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2036))

Abstract

Contours and surfaces are basic qualities which are processed by the visual system to aid the successful behavior of autonomous beings within the environment. There is increasing evidence that the two modal- ities of contours and surfaces are processed in separate, but interacting visual streams or sub-systems. Neurons at early stages in the visual sys- tem show strong responses only at locations of high contrast, such as edges, but only weak responses within homogeneous regions. Thus, for the processing and representation of surfaces, the visual system has to in- tegrate sparse local measurements into a dense, coherent representation. We suggest a mechanism of confidence-based filling-in, where a confi- dence measure ensures a robust selection of sparse contrast signals. The new mechanism supports the generation of surface representations which are invariant against size and shape transformation. The filling-in pro- cess is controlled by contour or boundary signals which stop the filling-in of contrast signals at region boundaries. Localized responses to contours are most often noisy and fragmented. We suggest a recurrent processing scheme for the extraction of contours that incorporates long-range con- nections. The recurrent long-range processing enhances coaligned activ- ity which is consistent within a more global context, while inconsistent noisy activity is suppressed. The capability of the model is shown for noisy synthesized and natural stimuli.

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Authors and Affiliations

  1. Abt. Neuroinformatik, Universität Ulm, D-89069, Ulm, Germany

    Thorsten Hansen & Heiko Neumann

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  1. Thorsten Hansen
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  2. Heiko Neumann
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Editors and Affiliations

  1. Centre of Informatics, SCET, University of Sunderland, St PetersWay, Sunderland SR6 0DD, UK

    Stefan Wermter

  2. Department of Computer Science, University of York, York, Y010 5DD, UK

    Jim Austin

  3. Institute for Adaptive and Neural Computation, University of Edinburgh, 5 Forrest Hill, Edinburgh, UK

    David Willshaw

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Hansen, T., Neumann, H. (2001). Neural Mechanisms for Representing Surface and Contour Features. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_10

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  • DOI: https://doi.org/10.1007/3-540-44597-8_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42363-8

  • Online ISBN: 978-3-540-44597-5

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