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Spiking Hierarchical Neural Network for Corner Detection

Kerr, D, McGinnity, TM, Coleman, SA, Wu, Qingxiang and Clogenson, M (2011) Spiking Hierarchical Neural Network for Corner Detection. In: International Conference on Neural Computation Theory and Applications, Paris, France. SciTePress. 1000 pp. [Conference contribution]

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DOI: 10.5220/0003682402300235


To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence corner detection is often used for this purpose. We present a new approach to corner detection inspired by the structure and behaviour of the human visual system, which uses spiking neural networks. Standard digital images are processed and converted to spikes in a manner similar to the processing that is performed in the retina. The spiking neural network performs edge and corner detection using receptive fields that are able to detect edges and corners of various orientations. The locations where neurons emit a spike indicate the positions of detected features. Results are presented using synthetic and real images.

Item Type:Conference contribution (Paper)
Keywords:Spiking neural network, Corner detection
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:20776
Deposited By: Dr Dermot Kerr
Deposited On:17 Jan 2012 14:41
Last Modified:09 Dec 2015 11:01

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