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

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 1990Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Acoustics, Speech, and Signal Processing, volume 38, pages 1,972-1,980 (issn: 0096-3518,

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Authors: Cristi, Roberto;

doi: 10.1109/29.103098

handle: 10945/56648

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

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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. >

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