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All Outputs (5)

Processing occlusions using elastic-net hierarchical MAX model of the visual cortex (2017)
Presentation / Conference
Alameer, A., Degenaar, P., & Nazarpour, K. (2017, July). Processing occlusions using elastic-net hierarchical MAX model of the visual cortex. Presented at 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Gdynia, Poland

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 oc... Read More about Processing occlusions using elastic-net hierarchical MAX model of the visual cortex.

Biologically-inspired object recognition system for recognizing natural scene categories (2016)
Presentation / Conference
Alameer, A., Degenaar, P., & Nazarpour, K. (2016, October). Biologically-inspired object recognition system for recognizing natural scene categories. Presented at International Conference for Students on Applied Engineering (ISCAE), Newcastle upon Tyne

Visual processing has attracted a lot of attention in the last decade. Hierarchical approaches for object recognition are gradually becoming widely-accepted. Generally, they are inspired by the ventral stream of human visual cortex, which is in charg... Read More about Biologically-inspired object recognition system for recognizing natural scene categories.

An exploratory study on the use of convolutional neural networks for object grasp classification
Presentation / Conference
Ghazaei, G., Alameer, A., Degenaar, P., Morgan, G., & Nazarpour, K. An exploratory study on the use of convolutional neural networks for object grasp classification. Presented at 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

The loss of hand profoundly affects an individual's quality of life. Prosthetic hands can provide a route to functional rehabilitation by allowing the amputees to undertake their daily activities. However, the performance of current artificial hands... Read More about An exploratory study on the use of convolutional neural networks for object grasp classification.

An elastic net-regularized HMAX model of visual processing
Presentation / Conference
Alameer, A., Ghazaeil, G., Degenaar, P., & Nazarpour, K. An elastic net-regularized HMAX model of visual processing. Presented at 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

The hierarchical MAX (HMAX) model of human visual system has been used in robotics and autonomous systems widely. However, there is still a stark gap between human and robotic vision in observing the environment and intelligently categorizing the obj... Read More about An elastic net-regularized HMAX model of visual processing.

Classification of handwritten Chinese numbers with convolutional neural networks
Presentation / Conference
Ameri, R., Alameer, A., Ferdowski, S., Abolghasemi, V., & Nazarpour, K. Classification of handwritten Chinese numbers with convolutional neural networks. Presented at 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

Deep learning methods have become the key ingredient in the field of computer vision; in particular, convolutional neural networks (CNNs). Appropriating the network architecture and data pre-processing have significant impact on performance. This pap... Read More about Classification of handwritten Chinese numbers with convolutional neural networks.