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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
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Li, Shaobai
d37641c3-d780-45e3-8062-94994060a13e
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Li, Shaobai, Dasmahapatra, Srinandan and Maharatna, Koushik (2015) Dynamical system approach for edge detection using coupled FitzHugh–Nagumo neurons. IEEE Transactions on Image Processing, 24 (12), 5206-5219. (doi:10.1109/TIP.2015.2467206).

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.

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Dynamical_System_Approach_to_Edge_Detection_Using_Coupled_FitzHugh-Nagumo_Neurons.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 20 March 2015
e-pub ahead of print date: 11 August 2015
Published date: December 2015
Organisations: Electronics & Computer Science

Identifiers

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

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Date deposited: 22 Apr 2016 08:43
Last modified: 14 Mar 2024 23:57

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Contributors

Author: Shaobai Li
Author: Srinandan Dasmahapatra
Author: Koushik Maharatna

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