By Topic

Markov chain Monte Carlo methods for clustering of image features

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 $31
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)
van Lieshout, M.N.M. ; Warwick Univ., Coventry, UK ; Baddeley, A.J.

The identification of centres of clustering is of interest in many areas of applications, for instance edge detector output has to be grouped into meaningful curves. The authors argue that stochastic geometry models are helpful both in providing models for clustering and as a prior distribution to combat overestimation of the number of clusters and to improve robustness. The general idea in connection with object recognition was proposed by Baddeley and van Lieshout [1993] and van Lieshout [1993]. Independently, in an epidemiological context, a different Gibbs sampler technique for detection of cluster centres in a Cox process was developed by Lawson [1993]

Published in:

Image Processing and its Applications, 1995., Fifth International Conference on

Date of Conference:

4-6 Jul 1995