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Model parameter estimation for 2D noncausal Gauss-Markov random fields

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3 Author(s)
R. Cusani ; INFOCOM Dept., Rome Univ., Italy ; E. Baccarelli ; S. Galli

An original procedure for estimating the model parameters of a noncausal Gauss-Markov random field (GMRF) from noisy observations is proposed. Starting from a suitable `local' representation of the field and taking into account the symmetry property of the so-called `potential fields' describing the GMRF, a linear equation system relating the model parameters to the (generally, nonstationary) 2D autocorrelation function (ACF) of the observed field is derived. Its solution for a known (or estimated) ACF directly gives the parameter estimates of the GMRF. The unknown variance of the eventually present observation noise can be also estimated jointly with the model parameters

Published in:

Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on

Date of Conference:

17-22 Sep 1995