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

Ali Alameer; Patrick Degenaar; Kianoush Nazarpour

doi:10.1109/INISTA.2017.8001150

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 proceeding › Conference contribution

Abstract / Description of output

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 language	English
Title of host publication	2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	163-167
Number of pages	5
ISBN (Electronic)	978-1-5090-5795-5
ISBN (Print)	978-1-5090-5796-2
DOIs	https://doi.org/10.1109/INISTA.2017.8001150
Publication status	Published - 8 Aug 2017
Event	2017 IEEE International Conference on INovations in Intelligent SysTems and Applications - Gdynia, Poland Duration: 3 Jul 2017 → 5 Jul 2017 http://inista.org/inista17/index.php

Conference

Conference	2017 IEEE International Conference on INovations in Intelligent SysTems and Applications
Abbreviated title	INISTA 2017
Country/Territory	Poland
City	Gdynia
Period	3/07/17 → 5/07/17
Internet address	http://inista.org/inista17/index.php

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https://ieeexplore.ieee.org/abstract/document/8001150Licence: All Rights Reserved

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Alameer, A., Degenaar, P., & Nazarpour, K. (2017). Processing occlusions using elastic-net hierarchical MAX model of the visual cortex. In 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 163-167). Institute of Electrical and Electronics Engineers (IEEE). Advance online publication. https://doi.org/10.1109/INISTA.2017.8001150

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title = "Processing occlusions using elastic-net hierarchical MAX model of the visual cortex",

abstract = "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 = "Ali Alameer and Patrick Degenaar and Kianoush Nazarpour",

year = "2017",

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day = "8",

doi = "10.1109/INISTA.2017.8001150",

language = "English",

isbn = "978-1-5090-5796-2",

pages = "163--167",

booktitle = "2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2017 IEEE International Conference on INovations in Intelligent SysTems and Applications, INISTA 2017 ; Conference date: 03-07-2017 Through 05-07-2017",

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}

Alameer, A, Degenaar, P & Nazarpour, K 2017, Processing occlusions using elastic-net hierarchical MAX model of the visual cortex. in 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA). Institute of Electrical and Electronics Engineers (IEEE), pp. 163-167, 2017 IEEE International Conference on INovations in Intelligent SysTems and Applications, Gdynia, Poland, 3/07/17. https://doi.org/10.1109/INISTA.2017.8001150

Processing occlusions using elastic-net hierarchical MAX model of the visual cortex. / Alameer, Ali; Degenaar, Patrick; Nazarpour, Kianoush.
2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA). Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 163-167.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Processing occlusions using elastic-net hierarchical MAX model of the visual cortex

AU - Alameer, Ali

AU - Degenaar, Patrick

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Y1 - 2017/8/8

N2 - 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.

AB - 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.

U2 - 10.1109/INISTA.2017.8001150

DO - 10.1109/INISTA.2017.8001150

M3 - Conference contribution

SN - 978-1-5090-5796-2

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EP - 167

BT - 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2017 IEEE International Conference on INovations in Intelligent SysTems and Applications

Y2 - 3 July 2017 through 5 July 2017

ER -

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

Abstract / Description of output

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