Cart (Loading....) | Create Account
Close category search window
 

The use of Gibbs random fields for image segmentation

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

3 Author(s)
Tao Wang ; Dept. of Comput. Sci. & Eng., Zhejiang Univ., Hangzhou, China ; Xinhua Zhuang ; Xiaoliang Xing

Presents a robust and adaptive technique for segmentation of a noisy image. The original image is modeled by an underlying Gibbs random field, and the noise is the mixture of an additive independent Gaussian noise and a salt or pepper noise. The processes of maximum a posteriori segmentation and maximum-likelihood estimation for the image model parameters are carried out simultaneously

Published in:

Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on

Date of Conference:

30 Aug-3 Sep 1992

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.