By Topic

Iterative segmentation algorithms using morphological operations

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)
Kelly, P.A. ; Dept. of Electr. & Comput. Eng., Massachusetts Univ., Amherst, MA, USA ; Chen, G.

The authors describe some iterative segmentation algorithms that combine statistical constraints represented in Markov random field models with deterministic constraints imposed by morphological operations. The goal is to produce segmentations that have high probability according to the Markov model and are smooth in the sense of being morphologically open and/or closed. The authors first present several algorithms for binary images, including one that produces a segmentation in which the set of one's is both open and closed. The latter algorithm is then extended to the case of multiregion images to produce a segmentation in which each region is open and closed.<>

Published in:

Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on  (Volume:5 )

Date of Conference:

27-30 April 1993