By Topic

Grayscale image edge detection based on pulse-coupled neural network and particle swarm optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jiesheng Wang ; Sch. of Electron. & Inf. Eng., Liaoning Univ. of Sci.&Technol., Anshan ; Fengwu Cong

Pulse coupled neural network (PCNN) was originally presented to explain the synchronous burst of the neurons in the cat visual cortex by Eckhorn. Because the parameters greatly affect the performance of PCNN, finding the optimal parameters becomes an onerous task. Particle swarm optimization (PSO) is a global stochastic evolutionary algorithm. It tries to find optimal regions of complex searching space through the interaction of particles in the population. A self-tuning optimized method for PCNN parameters based on PSO algorithm and it was used to detect edges in a gray image automatically and successfully. The effective of the proposed method is verified by simulation results, that is to say, the quality of the image edge detection is much better and parameters are set automatically.

Published in:

Control and Decision Conference, 2008. CCDC 2008. Chinese

Date of Conference:

2-4 July 2008