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A novel approach to robot vision using a hexagonal grid and spiking neural networks

Kerr, D, Coleman, SA, McGinnity, TM, Wu, Qingxiang and Clogenson, M. (2012) A novel approach to robot vision using a hexagonal grid and spiking neural networks. In: The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia. IEEE. 7 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6252591&isnumber=6252360

DOI: 10.1109/IJCNN.2012.6252591


Many robots use range data to obtain an almost 3-dimensional description of their environment. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Inspired by the structure and behaviour of the human visual system, we present an approach to feature extraction in range data using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields; this approach is a step towards developing a robotic eye that closely mimics the human eye. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonally shaped receptive fields, performance is improved over standard rectangular shaped receptive fields.

Item Type:Conference contribution (Paper)
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:22993
Deposited By: Dr Dermot Kerr
Deposited On:13 Aug 2012 11:37
Last Modified:13 Aug 2012 11:37

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