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A Biologically Inspired Model for Occluded Patterns
Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
2011 (English)In: Neural Information Processing: proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011,  Shanghai, China, November 2011., 2011, 88-96 p.Conference paper (Refereed)
Abstract [en]

In this paper a biologically-inspired model for partly occluded patterns is proposed. The model is based on the hypothesis that in human visual system occluding patterns play a key role in recognition as well as in reconstructing internal representation for a pattern’s occluding parts. The proposed model is realized with a bidirectional hierarchical neural network. In this network top-down cues, generated by direct connections from the lower to higher levels of hierarchy, interact with the bottom-up information, generated from the un-occluded parts, to recognize occluded patterns. Moreover, positional cues of the occluded as well as occluding patterns, that are computed separately but in the same network, modulate the top-down and bottom-up processing to reconstruct the occluded patterns. Simulation results support the presented hypothesis as well as effectiveness of the model in providing a solution to recognition of occluded patterns. The behavior of the model is in accordance to the known human behavior on the occluded patterns.

Place, publisher, year, edition, pages
2011. 88-96 p.
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 7062
Keyword [en]
Vision, Neural network model, Occluded patterns, Biologically-inspired.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-77027DOI: 10.1007/978-3-642-24955-6_11ISBN: 978-3-642-24954-9 (print)ISBN: 978-3-642-24955-6 (online)OAI: diva2:524480
18th International Conference on Neural Information Processing, ICONIP 2011; Shanghai; China
Available from: 2012-06-13 Created: 2012-05-02 Last updated: 2014-10-16Bibliographically approved

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Saifullah, Mohammad
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