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

Ali Alameer, Patrick Degenaar, Kianoush Nazarpour

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.
Original languageUndefined/Unknown
Title of host publicationINnovations in Intelligent SysTems and Applications (INISTA), 2017 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)978-1-5090-5795-5
ISBN (Print)978-1-5090-5796-2
Publication statusPublished - 08 Aug 2017

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