Biologically Inspired Edge Detection

Dermot Kerr; SA Coleman; TM McGinnity; Qingxiang Wu; Marine Clogenson

doi:10.1109/ISDA.2011.6121755

Biologically Inspired Edge Detection

Dermot Kerr, SA Coleman, TM McGinnity, Qingxiang Wu, Marine Clogenson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

157 Downloads (Pure)

Abstract

Inspired by the structure and behaviour of the human visual system, we present an approach to edge detection 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. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonal shaped receptive fields, performance is improved over standard rectangular shaped receptive fields.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	802-807
Number of pages	1000
ISBN (Print)	978-1-4577-1676-8
DOIs	https://doi.org/10.1109/ISDA.2011.6121755
Publication status	Published (in print/issue) - 3 Jan 2012
Event	11th International Conference on Intelligent Systems Design and Applications (ISDA) - Cordoba, Spain Duration: 3 Jan 2012 → …

Conference

Conference	11th International Conference on Intelligent Systems Design and Applications (ISDA)
Period	3/01/12 → …

Keywords

Edge detection
Spiking neural network

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISDA.2011.6121755

ISDAFinal published version, 943 KB

Cite this

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title = "Biologically Inspired Edge Detection",

abstract = "Inspired by the structure and behaviour of the human visual system, we present an approach to edge detection 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. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonal shaped receptive fields, performance is improved over standard rectangular shaped receptive fields.",

keywords = "Edge detection, Spiking neural network",

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AU - Kerr, Dermot

AU - Coleman, SA

AU - McGinnity, TM

AU - Wu, Qingxiang

AU - Clogenson, Marine

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N2 - Inspired by the structure and behaviour of the human visual system, we present an approach to edge detection 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. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonal shaped receptive fields, performance is improved over standard rectangular shaped receptive fields.

AB - Inspired by the structure and behaviour of the human visual system, we present an approach to edge detection 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. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonal shaped receptive fields, performance is improved over standard rectangular shaped receptive fields.

KW - Edge detection

KW - Spiking neural network

U2 - 10.1109/ISDA.2011.6121755

DO - 10.1109/ISDA.2011.6121755

M3 - Conference contribution

SN - 978-1-4577-1676-8

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EP - 807

BT - Unknown Host Publication

PB - IEEE

T2 - 11th International Conference on Intelligent Systems Design and Applications (ISDA)

Y2 - 3 January 2012

ER -

Biologically Inspired Edge Detection

Abstract

Conference

Keywords

UN SDGs

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