By Topic

Bayesian image segmentation based on an inhomogenous hidden Markov random field

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)
Junxi Sun ; Shanghai Jiaotong Univ., Changchun, China ; Dongbing Gu

This paper introduces a Bayesian image segmentation algorithm with the consideration of label scale variability in many images. An inhomogeneous hidden Markov random field is adopted in this algorithm to model the label scale variability as a prior probability. An EM algorithm is developed to estimate parameters for both the prior probability and likelihood probability. The image segmentation is established by a MAP estimator. Different images are tested to verify our algorithm. Comparisons with other segmentation algorithms are made. The segmentation results show that our algorithm has better performance than others.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:1 )

Date of Conference:

23-26 Aug. 2004