Abstract
In this paper the visual processing architecture is assumed to be hierarchical in structure with units within this network receiving both feed-forward and feedback connections. We propose a neural computational model of visual system, which is based on the hierarchical structure of feedback selectiveness of visual attention information and feature integration theory. The proposed model consists of three stages. Visual image input is first decomposed into a set of topographic feature maps in a massively parallel method at the saliency stage. The feature integration stage is based on the feature integration theory, which is a representative theory for explaining all phenomena occurring in visual system as a consistent process. At last stage through feedback selection, the saliency stimulus is localized in each feature map. We carried out computer simulation and conformed that the proposed model is feasible and effective.
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© 2004 Springer-Verlag Berlin Heidelberg
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Zhao, L., Luo, S. (2004). Feedback Selective Visual Attention Model Based on Feature Integration Theory. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_77
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DOI: https://doi.org/10.1007/978-3-540-28648-6_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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