Markov and recursive least squares methods for the estimation of data with discontinuities

Author

Cristi, Roberto

Author_Institution

Dept. of Electr. & Comput. Eng., US Naval Postgraduate Sch., Monterey, CA, USA

Volume

38

Issue

11

fYear

1990

fDate

11/1/1990 12:00:00 AM

Firstpage

1972

Lastpage

1980

Abstract

An algorithm is presented for smoothing data piecewise modeled by linear equations within regions of a one-dimensional or two-dimensional field, from measurements corrupted by additive noise. Its main feature is the combination of Markov random field (MRF) models with recursive least squares (RLS) techniques in order to estimate the model parameters within the regions. Applications to one-dimensional and two-dimensional data are given, with particular emphasis on the segmentation of images with piecewise constant intensity levels

Keywords

Markov processes; least squares approximations; picture processing; random noise; Markov random field; additive noise; image segmentation; piecewise constant intensity levels; recursive least squares methods; Additive noise; Equations; Least squares approximation; Least squares methods; Markov random fields; Noise measurement; Parameter estimation; Recursive estimation; Resonance light scattering; Smoothing methods;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.103098

Filename

103098