NDLI: Dynamical System Approach for Edge Detection Using Coupled FitzHugh

Content Provider	IEEE Xplore Digital Library
Author	Shaobai Li Dasmahapatra, S. Maharatna, K.
Copyright Year	1992
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.
Sponsorship	IEEE Signal Processing Society
Starting Page	5206
Ending Page	5219
Page Count	14
File Size	4638366
File Format	PDF
ISSN	10577149
Volume Number	24
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Neurons Mathematical model Gray-scale Couplings Trajectory Biological neural networks reaction-diffusion system Excitability Lyapunov exponent edge detection FitzHugh-Nagumo model
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Stabilization of coupled reaction-diffusion CNN

Synchronization and control of a network of coupled reaction diffusion systems of generalized FitzHugh-Nagumo type

Synchronization of fitzhugh-nagumo neural network in external electrical fields

Synchronization of two uncoupled Fitzhugh-Nagumo neurons via nonlinear control

System size coherence resonance in coupled FitzHugh-Nagumo models,” Europhys (2003)

Investigation of microcontroller based model of FitzHugh-Nagumo neuron

Adaptive Perona–Malik Model Based on the Variable Exponent for Image Denoising

Inphase and Antiphase Synchronization in a Delay-Coupled System With Applications to a Delay-Coupled FitzHugh–Nagumo System

Chaos Synchronization of Coupled FitzHugh-Nagumo Neurons Via Adaptive Sliding Mode Control

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons

Similar Documents

Stabilization of coupled reaction-diffusion CNN

Synchronization and control of a network of coupled reaction diffusion systems of generalized FitzHugh-Nagumo type

Synchronization of fitzhugh-nagumo neural network in external electrical fields

Synchronization of two uncoupled Fitzhugh-Nagumo neurons via nonlinear control

System size coherence resonance in coupled FitzHugh-Nagumo models,” Europhys (2003)

Investigation of microcontroller based model of FitzHugh-Nagumo neuron

Adaptive Perona–Malik Model Based on the Variable Exponent for Image Denoising

Inphase and Antiphase Synchronization in a Delay-Coupled System With Applications to a Delay-Coupled FitzHugh–Nagumo System

Chaos Synchronization of Coupled FitzHugh-Nagumo Neurons Via Adaptive Sliding Mode Control

Dynamical System Approach for Edge Detection Using Coupled FitzHugh–Nagumo Neurons