A Bayesian approach to 2D non minimum phase AR identification

Author

Jacovitti, Giovanni ; Neri, Alessandro

Author_Institution

Info-Com. Dept., Rome Univ., Italy

fYear

1990

fDate

10-12 Oct. 1990

Firstpage

79

Lastpage

83

Abstract

The authors deal with estimation of autoregressive (AR) noncausal models of bidimensional signals. The problem of factorizing an image into an excitation with a given marginal p.d.f. and a IIR filter is formulated in a Bayesian conceptual framework. The proposed solution is an iterative procedure for the minimization of the a posteriori risk associated to a given cost function. The procedure implies the inversion of a Toeplitz-block-Toeplitz covariance matrix and the iterated solution of a set of normal equations associated with a nonlinear estimation stage.<>

Keywords

Bayes methods; computerised picture processing; digital filters; iterative methods; parameter estimation; 2D nonminimum phase autoregressive identification; Bayesian conceptual framework; IIR filter; Toeplitz-block-Toeplitz covariance matrix; a posteriori risk; bidimensional signals; cost function; image processing; iterative procedure; marginal PDF; matrix inversion; noncausal models; nonlinear estimation stage; normal equations; Bayesian methods; Cost function; Covariance matrix; Deconvolution; Entropy; Equations; Finite impulse response filter; Higher order statistics; Phase estimation; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Spectrum Estimation and Modeling, 1990., Fifth ASSP Workshop on

Conference_Location

Rochester, NY, USA

Type

conf

DOI

10.1109/SPECT.1990.205550

Filename

205550