Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/8083
Title: A computer vision model for visual-object-based attention and eye movements
Authors: Sun, Y
Fisher, R
Wang, F
Gomes, HM
Keywords: Visual-object-based competition;Space-based attention;Object-based attention;Group-based attention;Foveated imaging;Attention-driven eye movements
Issue Date: 2008
Publisher: Elsevier
Citation: Computer Vision and Image Understanding, 112(2), 126 - 142, 2008
Abstract: This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.
Description: This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.
URI: http://www.sciencedirect.com/science/article/pii/S1077314208000167
http://bura.brunel.ac.uk/handle/2438/8083
DOI: http://dx.doi.org/10.1016/j.cviu.2008.01.005
ISSN: 1077-3142
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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