By Topic

Learning structural and corruption information from samples for Markov random field binary image reconstruction

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)
Milun, D. ; Dept. of Comput. Sci., State Univ. of New York, Buffalo, NY, USA ; Sher, D.

The authors have advanced Markov random field research by addressing the issue of obtaining a reasonable, nontrivial, noise model. They address this issue by looking at original images together with noisy imagery, and so creating a probability distribution for pairs of neighborhoods across both images. This models the noise within the MRF probability distribution, and provides an easy way to generate Markov random fields for annealing or other relaxation methods

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