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Bottom-Up Visual Attention for Still Images: A Global View

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From Human Attention to Computational Attention

Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS,volume 10))

Abstract

Studies in neuroscience are suggesting that human visual attention is enhanced through a process of competing interactions among neurons representing all of the stimuli present in the visual field. This chapter explores current avenues of research into models of visual attention that reflect human behaviour. The approaches are categorised broadly into feature-based and structural methods exposing advantages and disadvantages of both.

The potential benefits that can arise from a model of attention are manifold and include applications to visual inspection in manufacturing processes, medical diagnosis, spotting security breaches, removing redundancy in data, various targeting applications and many others.

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Author information

Authors and Affiliations

  1. University College London, Torrington Place, London, WC1E 7JE, UK

    Fred Stentiford

Authors
  1. Fred Stentiford
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Correspondence to Fred Stentiford .

Editor information

Editors and Affiliations

  1. Numediart Institute, University of Mons, Mons, Belgium

    Matei Mancas

  2. Columbia University, New York, New York, USA

    Vincent P. Ferrera

  3. Numediart Institute, University of Mons, MONS, Belgium

    Nicolas Riche

  4. King's College, LONDON, United Kingdom

    John G. Taylor

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Stentiford, F. (2016). Bottom-Up Visual Attention for Still Images: A Global View. In: Mancas, M., Ferrera, V., Riche, N., Taylor, J. (eds) From Human Attention to Computational Attention. Springer Series in Cognitive and Neural Systems, vol 10. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3435-5_8

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