By Topic

Parameter Selection of Generalized Fuzzy Entropy-Based Thresholding Segmentation Method with 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)
Bo Lei ; Sch. of Electron. Eng., Xidian Univ., Xi'an ; Jiu-Lun Fan

Image thresholding method based on generalized fuzzy entropy segments the image using the principle that the membership degree of the threshold point is equal to m (0<m<1), better segmentation result can be obtained than that of traditional fuzzy entropy method, especially for images with bad illumination. The main problem of this method is how to determine the parameter m effectively. In this paper, we use particle swarm optimization to solve it. Based on an image segmentation quality evaluation criterion and the maximum fuzzy entropy criterion, using particle swarm optimization, the optimal parameter m and the membership function parameters (a, b, d) is automatically determined respectively, realizing the aim of automatic selection the threshold in generalized fuzzy entropy-based image segmentation method. Experiment results show that our method can obtain better segmentation results than that of traditional fuzzy entropy based method.

Published in:

Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP '08 International Conference on

Date of Conference:

15-17 Aug. 2008