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.
|Original language||English (US)|
|Number of pages||16|
|Journal||IEEE Transactions on Aerospace and Electronic Systems|
|State||Published - Jul 1 2003|
ASJC Scopus subject areas
- Aerospace Engineering
- Electrical and Electronic Engineering