TY - GEN
T1 - Multiscale Bayesian estimation in Pairwise Markov Trees
AU - Desbouvries, Francois
AU - Lecomte, Jean
N1 - Publisher Copyright:
© 2004 EUSIPCO.
PY - 2015/4/3
Y1 - 2015/4/3
N2 - An important problem in multiresolution analysis of signals and images consists in estimating hidden random variables (r.v.) x = [x s ] s∨S from observed ones y = [y s ] s∨S . This is done classically in the context of Hidden Markov Trees (HMT). In particular, a smoothing Kalman-like algorithm has been proposed by Chou et al. in the linear Gaussian case. In this paper we extend this algorithm to the more general framework of Pairwise Markov Trees (PMT).
AB - An important problem in multiresolution analysis of signals and images consists in estimating hidden random variables (r.v.) x = [x s ] s∨S from observed ones y = [y s ] s∨S . This is done classically in the context of Hidden Markov Trees (HMT). In particular, a smoothing Kalman-like algorithm has been proposed by Chou et al. in the linear Gaussian case. In this paper we extend this algorithm to the more general framework of Pairwise Markov Trees (PMT).
M3 - Conference contribution
AN - SCOPUS:84979879774
T3 - European Signal Processing Conference
SP - 1437
EP - 1440
BT - 2004 12th European Signal Processing Conference, EUSIPCO 2004
PB - European Signal Processing Conference, EUSIPCO
T2 - 12th European Signal Processing Conference, EUSIPCO 2004
Y2 - 6 September 2004 through 10 September 2004
ER -