Biologically Inspired Edge Detection using Spiking Neural Networks and Hexagonal Images

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

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

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Inspired by the structure and behaviour of the human visual system, we extend existing work using spiking neural networks for edge detection with a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation before being processed with a spiking neural network with scalable hexagonally shaped receptive fields. The performance is compared with different sized receptive fields implemented on standard rectangular images. Results illustrate that using hexagonal-shaped receptive fields provides improved performance over a range of scales compared with standard rectangular shaped receptive fields and images.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSciTePress
Pages381-384
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
  • Edge detection
  • Multi-scale hexagonal receptive fields

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