TY - GEN
T1 - Restoring hidden non stationary process using triplet partially Markov chain with long memory noise
AU - Pieczynski, Wojciech
AU - Lanchantin, Pierre
PY - 2005/1/1
Y1 - 2005/1/1
N2 - The hidden Markov chains (HMC), which are widely used in different data restoration problems, have recently been generalised to pairwise partially Markov chains (PPMC), in which the distribution of the observed chain conditional on the hidden one is of any form. In particular, long-memory noise cases can be dealt with. The aim of this paper is to propose a parameter estimation method and to show, via experiments, that unsupervised PPMC based image segmentation can perform better, when the noise is a long-memory one, than the classical HMC based methods.
AB - The hidden Markov chains (HMC), which are widely used in different data restoration problems, have recently been generalised to pairwise partially Markov chains (PPMC), in which the distribution of the observed chain conditional on the hidden one is of any form. In particular, long-memory noise cases can be dealt with. The aim of this paper is to propose a parameter estimation method and to show, via experiments, that unsupervised PPMC based image segmentation can perform better, when the noise is a long-memory one, than the classical HMC based methods.
U2 - 10.1109/ssp.2005.1628686
DO - 10.1109/ssp.2005.1628686
M3 - Conference contribution
AN - SCOPUS:33947175123
SN - 0780394046
SN - 9780780394049
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 709
EP - 713
BT - 2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
PB - IEEE Computer Society
T2 - 2005 IEEE/SP 13th Workshop on Statistical Signal Processing
Y2 - 17 July 2005 through 20 July 2005
ER -