Spiking Hierarchical Neural Network for Corner Detection

D Kerr, TM McGinnity, SA Coleman, Qingxiang Wu, M Clogenson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
35 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSciTePress
Pages230-235
Number of pages1000
DOIs
Publication statusPublished - 2011
EventInternational Conference on Neural Computation Theory and Applications - Paris, France
Duration: 1 Jan 2011 → …

Conference

ConferenceInternational Conference on Neural Computation Theory and Applications
Period1/01/11 → …

Keywords

  • Spiking neural network
  • Corner detection

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  • Cite this

    Kerr, D., McGinnity, TM., Coleman, SA., Wu, Q., & Clogenson, M. (2011). Spiking Hierarchical Neural Network for Corner Detection. In Unknown Host Publication (pp. 230-235). SciTePress. https://doi.org/10.5220/0003682402300235