The problem of clutter region identification based on Markov random field (MRF) models is addressed. Observations inside each clutter region are assumed homogenous, i.e., observations follow a single probability distribution. Our goal is to partition clutter scenes into homogenous regions and to determine this underlying probability distribution. Metropolis-Hasting and reversible jump Markov chain (RJMC) algorithms are used to search for solutions based on the maximum a posteriori (MAP) criterion. Several examples illustrate the performance of our algorithm.
Statistical characterization of clutter scenes based on a Markov random field model
IEEE Transactions on Aerospace and Electronic Systems ; 39 , 3 ; 1035-1050
01.07.2003
4317180 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Statistical Characterization of Clutter Scenes Based on a Markov Random Field Model
Online Contents | 2003
|Markov Random Field model for single image defogging
IEEE | 2013
|MARKOV RANDOM FIELD MODEL FOR SINGLE IMAGE DEFOGGING
British Library Conference Proceedings | 2013
|