By Topic

Unsupervised texture segmentation based on the modified Markov random field model

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)
Y. Xiaohan ; Graphic Arts Lab., Tech. Res. Centre of Finland, Espoo, Finland ; J. Yla-Jaaski

The Gaussian-Markov random field (MRF) model is a very useful technique for image processing, such as feature extraction and data compression. However its strict stability condition makes the model identification complex. The major problem is the choice of a proper support region for the model. In this paper a new model is proposed which is based on the MRF model and called the modified Gaussian-Markov random field model. It is not an optimal MRF model but has a very useful property, namely decorrelation. A stable modified MRF model always exists even if a stable MRF model does not exist on the given support region. Applications to texture segmentation are also presented

Published in:

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

Date of Conference:

30 Aug-3 Sep 1992