By Topic

Fuzzy c-partition using particle swarm optimization algorithm

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
$33 $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)
O. Assas ; Université Sidi Mohamed Ben Abdellah Faculté des, sciences Dhar El Mehraz Laboratoire d'Electronique, Signaux - Systèmes et Informatique(LESSI) Fès, Maroc ; Kheir Benmahammed

The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper we applied fuzzy entropy in image segmentation, used it to select the fuzzy region of membership function automatically so that an image can be transformed into fuzz domain with maximum fuzzy entropy, and implemented particle swarm optimization algorithm to find the optimal combination of fuzzy parameters. The proposed fast approach has been tested on many images for example the processing time of tri level thresholding of each image is reduced from more than 22 min to less than 0.5 s. The Fuzzy c-partition entropy using PSO algorithm perform equally well in terms of the quality of image segmentation and leads to a good visual result.

Published in:

Complex Systems (ICCS), 2012 International Conference on

Date of Conference:

5-6 Nov. 2012