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International Workshop on Attention in Cognitive Systems

WAPCV 2007: Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint pp 141–154Cite as

The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4840))

Abstract

We recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object scenes. SAIM can simulate a wide range of experimental evidence on normal and disordered attention. In its earlier form, SAIM could only process black and white images. The present paper tackles this important shortcoming by extending SAIM with a biologically plausible feature extraction, using Gabor filters and coding colour information in HSV-colour space. With this extension SAIM proved able to select and recognize objects in natural multiple-object colour scenes. Moreover, this new version still mimicked human data on visual search tasks. These results stem from the competitive parallel interactions that characterize processing in SAIM.

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Author information

Authors and Affiliations

  1. Behavioural and Brain Sciences Centre, University of Birmingham, Birmingham B15 2TT, United Kingdom

    Dietmar Heinke, Andreas Backhaus, Yarou Sun & Glyn W. Humphreys

Authors
  1. Dietmar Heinke
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  2. Andreas Backhaus
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  3. Yarou Sun
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  4. Glyn W. Humphreys
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Editor information

Editors and Affiliations

  1. Joanneum Research, Forschungsgesellschaft mbH, Computational Perception Group,, Institute of Digital Image Processing, Wastiangasse 6, 8010, Graz, Austria

    Lucas Paletta

  2. Autonomous Intelligent Systems (AIS), Autonomous Robots Department, Fraunhofer Institute, Schloss Birlinghoven, 53754, Sankt Augustin, Germany

    Erich Rome

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Heinke, D., Backhaus, A., Sun, Y., Humphreys, G.W. (2007). The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images. In: Paletta, L., Rome, E. (eds) Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint. WAPCV 2007. Lecture Notes in Computer Science(), vol 4840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77343-6_9

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  • DOI: https://doi.org/10.1007/978-3-540-77343-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77342-9

  • Online ISBN: 978-3-540-77343-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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