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

Alameer, A; Degenaar, P; Nazarpour, K

doi:10.1109/INISTA.2017.8001150

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

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