Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System

Qingxiang Wu, TM McGinnity, Liam Maguire, German Valderrama, Patrick Dempster

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

12 Citations (Scopus)

Abstract

The human visual system demonstrates powerful image processing functionalities. Inspired by the visual system, a spiking neural network is proposed to segment visual images. The network is constructed in the two parts. The first part is a spiking neural network which is composed of photon receptors, cone and rod cells, and ON/OFF ganglion cells. Colour features can be extracted and passed through different ON/OFF pathways. The second part is a BP neural network which is trained to recognize the colour features and segment the visual image. The network has been successfully applied to segment leukocytes from blood smeared images.
LanguageEnglish
Title of host publicationUnknown Host Publication
EditorsD.-S. Huang
Place of PublicationBerlin Heidelberg
Pages49-57
Number of pages8
DOIs
Publication statusPublished - 1 Aug 2010
Eventinternational conference on Intelligent Computing (ICIC2010) - Changsha, China
Duration: 1 Aug 2010 → …

Conference

Conferenceinternational conference on Intelligent Computing (ICIC2010)
Period1/08/10 → …

Fingerprint

Image segmentation
Color
Neural networks
Cones
Image processing
Blood
Photons

Keywords

  • Spiking neural networks
  • image segmentation
  • visual system
  • visual image

Cite this

Wu, Q., McGinnity, TM., Maguire, L., Valderrama, G., & Dempster, P. (2010). Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System. In D-S. Huang (Ed.), Unknown Host Publication (pp. 49-57). Berlin Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_7
Wu, Qingxiang ; McGinnity, TM ; Maguire, Liam ; Valderrama, German ; Dempster, Patrick. / Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System. Unknown Host Publication. editor / D.-S. Huang. Berlin Heidelberg, 2010. pp. 49-57
@inproceedings{3b7f88ce2e7f4d27974ac7890190f722,
title = "Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System",
abstract = "The human visual system demonstrates powerful image processing functionalities. Inspired by the visual system, a spiking neural network is proposed to segment visual images. The network is constructed in the two parts. The first part is a spiking neural network which is composed of photon receptors, cone and rod cells, and ON/OFF ganglion cells. Colour features can be extracted and passed through different ON/OFF pathways. The second part is a BP neural network which is trained to recognize the colour features and segment the visual image. The network has been successfully applied to segment leukocytes from blood smeared images.",
keywords = "Spiking neural networks, image segmentation, visual system, visual image",
author = "Qingxiang Wu and TM McGinnity and Liam Maguire and German Valderrama and Patrick Dempster",
year = "2010",
month = "8",
day = "1",
doi = "10.1007/978-3-642-14922-1_7",
language = "English",
pages = "49--57",
editor = "D.-S. Huang",
booktitle = "Unknown Host Publication",

}

Wu, Q, McGinnity, TM, Maguire, L, Valderrama, G & Dempster, P 2010, Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System. in D-S Huang (ed.), Unknown Host Publication. Berlin Heidelberg, pp. 49-57, international conference on Intelligent Computing (ICIC2010), 1/08/10. https://doi.org/10.1007/978-3-642-14922-1_7

Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System. / Wu, Qingxiang; McGinnity, TM; Maguire, Liam; Valderrama, German; Dempster, Patrick.

Unknown Host Publication. ed. / D.-S. Huang. Berlin Heidelberg, 2010. p. 49-57.

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

TY - GEN

T1 - Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System

AU - Wu, Qingxiang

AU - McGinnity, TM

AU - Maguire, Liam

AU - Valderrama, German

AU - Dempster, Patrick

PY - 2010/8/1

Y1 - 2010/8/1

N2 - The human visual system demonstrates powerful image processing functionalities. Inspired by the visual system, a spiking neural network is proposed to segment visual images. The network is constructed in the two parts. The first part is a spiking neural network which is composed of photon receptors, cone and rod cells, and ON/OFF ganglion cells. Colour features can be extracted and passed through different ON/OFF pathways. The second part is a BP neural network which is trained to recognize the colour features and segment the visual image. The network has been successfully applied to segment leukocytes from blood smeared images.

AB - The human visual system demonstrates powerful image processing functionalities. Inspired by the visual system, a spiking neural network is proposed to segment visual images. The network is constructed in the two parts. The first part is a spiking neural network which is composed of photon receptors, cone and rod cells, and ON/OFF ganglion cells. Colour features can be extracted and passed through different ON/OFF pathways. The second part is a BP neural network which is trained to recognize the colour features and segment the visual image. The network has been successfully applied to segment leukocytes from blood smeared images.

KW - Spiking neural networks

KW - image segmentation

KW - visual system

KW - visual image

UR - http://www.springerlink.com/content/3406131q37089x37/

UR - http://www.springerlink.com/content/3406131q37089x37/

U2 - 10.1007/978-3-642-14922-1_7

DO - 10.1007/978-3-642-14922-1_7

M3 - Conference contribution

SP - 49

EP - 57

BT - Unknown Host Publication

A2 - Huang, D.-S.

CY - Berlin Heidelberg

ER -

Wu Q, McGinnity TM, Maguire L, Valderrama G, Dempster P. Colour Image Segmentation Based on a Spiking Neural Network Model Inspired by the Visual System. In Huang D-S, editor, Unknown Host Publication. Berlin Heidelberg. 2010. p. 49-57 https://doi.org/10.1007/978-3-642-14922-1_7