The University of Southampton
University of Southampton Institutional Repository

Dynamical system approach for edge detection using coupled FitzHugh–Nagumo neurons

Dynamical system approach for edge detection using coupled FitzHugh–Nagumo neurons
Dynamical system approach for edge detection using coupled FitzHugh–Nagumo neurons
The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.
1057-7149
5206-5219
Li, Shaobai
d37641c3-d780-45e3-8062-94994060a13e
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Li, Shaobai
d37641c3-d780-45e3-8062-94994060a13e
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698

Li, Shaobai, Maharatna, Koushik and Dasmahapatra, Srinandan (2015) Dynamical system approach for edge detection using coupled FitzHugh–Nagumo neurons. IEEE Transactions on Image Processing, 24 (12), 5206-5219.

Record type: Article

Abstract

The prospect of emulating the impressive computational capabilities of biological systems has led to considerable interest in the design of analog circuits that are potentially implementable in very large scale integration CMOS technology and are guided by biologically motivated models. For example, simple image processing tasks, such as the detection of edges in binary and grayscale images, have been performed by networks of FitzHugh-Nagumo-type neurons using the reaction-diffusion models. However, in these studies, the one-to-one mapping of image pixels to component neurons makes the size of the network a critical factor in any such implementation. In this paper, we develop a simplified version of the employed reaction-diffusion model in three steps. In the first step, we perform a detailed study to locate this threshold using continuous Lyapunov exponents from dynamical system theory. Furthermore, we render the diffusion in the system to be anisotropic, with the degree of anisotropy being set by the gradients of grayscale values in each image. The final step involves a simplification of the model that is achieved by eliminating the terms that couple the membrane potentials of adjacent neurons. We apply our technique to detect edges in data sets of artificially generated and real images, and we demonstrate that the performance is as good if not better than that of the previous methods without increasing the size of the network.

PDF
Dynamical_System_Approach_to_Edge_Detection_Using_Coupled_FitzHugh-Nagumo_Neurons.pdf - Accepted Manuscript
Download (1MB)

More information

e-pub ahead of print date: 11 August 2015
Published date: December 2015
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 393164
URI: https://eprints.soton.ac.uk/id/eprint/393164
ISSN: 1057-7149
PURE UUID: c3cdb9e9-caa7-46d1-8a16-577573104272

Catalogue record

Date deposited: 22 Apr 2016 08:43
Last modified: 10 Nov 2017 21:32

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×