MULTISCALE BAYESIAN ESTIMATION IN PAIRWISE MARKOV TREES (ThuAmOR5)

Author(s) : François Desbouvries (Institut National des Télécommunications, France) Jean Lecomte (Institut National des Télécommunications, France)

Abstract : An important problem in multiresolution analysis of signals and images consists in estimating hidden random variables $x = \{x_s\}_{s \in S}$ from observed ones $y = \{y_s\}_{s \in S}$. This is done classically in the context of Hidden Markov Trees (HMT). In particular, a smoothing Kalman-like algorithm has been proposed by Chou {\sl et al.} in the linear Gaussian case. In this paper we extend this algorithm to the more general framework of Pairwise Markov Trees (PMT).

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