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A biologically inspired spiking model of visual processing for image feature detection

Kerr, D, McGinnity, TM, Coleman, SA and Clogenson, M (2015) A biologically inspired spiking model of visual processing for image feature detection. Neurocomputing, X . [Journal article]

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DOI: 10.1016/j.neucom.2015.01.011


To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:31075
Deposited By: Dr Sonya Coleman
Deposited On:06 Mar 2015 09:54
Last Modified:06 Mar 2015 09:54

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