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Lookup NU author(s): Dr Ali Alameer,
Professor Patrick Degenaar,
Professor Kianoush Nazarpour
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© 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.
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
Online publication date: 08/08/2017
Acceptance date: 03/07/2017
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