Biologically Inspired Edge Detection using Spiking Neural Networks and Hexagonal Images

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

doi:10.5220/0003682103810384

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 proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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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 language	English
Title of host publication	Unknown Host Publication
Publisher	SciTePress
Pages	381-384
Number of pages	1000
DOIs	https://doi.org/10.5220/0003682103810384
Publication status	Published (in print/issue) - 2011
Event	International Conference on Neural Computation Theory and Applications - Paris, France Duration: 1 Jan 2011 → …

Conference

Conference	International Conference on Neural Computation Theory and Applications
Period	1/01/11 → …

Keywords

Spiking neural network
Edge detection
Multi-scale hexagonal receptive fields

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10.5220/0003682103810384

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Cite this

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title = "Biologically Inspired Edge Detection using Spiking Neural Networks and Hexagonal Images",

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.",

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AU - Clogenson, M

AU - Kerr, D

AU - McGinnity, TM

AU - Coleman, SA

AU - Wu, Qingxiang

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Spiking neural network

KW - Edge detection

KW - Multi-scale hexagonal receptive fields

U2 - 10.5220/0003682103810384

DO - 10.5220/0003682103810384

M3 - Conference contribution

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EP - 384

BT - Unknown Host Publication

PB - SciTePress

T2 - International Conference on Neural Computation Theory and Applications

Y2 - 1 January 2011

ER -

Biologically Inspired Edge Detection using Spiking Neural Networks and Hexagonal Images

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