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Processing occlusions using elastic-net hierarchical MAX model of the visual cortex

Lookup NU author(s): Dr Ali Alameer, Professor Patrick Degenaar, Dr Kianoush Nazarpour

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Abstract

© 2017 IEEE. Humans can recognise objects under partial occlusion. Machine-based approaches cannot reliably recognise objects and scenes in the presence of occlusion. This paper investigates the use of the elastic net hierarchical MAX (En-HMAX) model to handle occlusions. Our experiments show that the En-HMAX model achieves an accuracy of ∼70%, when ∼50% artificial occlusions are applied to the centre of the visual object-field. Furthermore, when the same percentage of occlusion is applied to the peripheral, the model reports higher accuracies. A similar degree of robustness has been observed when recognising scenes. The results suggest that cortex-like models, such as the En-HMAX are reliable for solving the occlusion challenge.


Publication metadata

Author(s): Alameer A, Degenaar P, Nazarpour K

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2017

Year of Conference: 2017

Pages: 163-167

Online publication date: 08/08/2017

Acceptance date: 03/07/2017

Publisher: IEEE

URL: https://doi.org/10.1109/INISTA.2017.8001150

DOI: 10.1109/INISTA.2017.8001150

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509057955


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