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

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Cristi_Markov and Recursive.pdf (1.1 MB)
Authors
Cristi, Roberto
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Date of Issue
1990-11
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IEEE
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Abstract
An algorithm is presented for smoothing data piecewise modeled by linear equations within regions of a one-dimensional (1-D) or two-dimensional (2-D) 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 1-D and 2-D data are given, with particular empha­sis on the segmentation of images with piecewise constant intensity lev­els.
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Article
Description
URI
https://hdl.handle.net/10945/56648
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Department
Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
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Office of Naval Research
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Format
9 p.
Citation
R. Cristi, "Markov and recursive least squares methods for the estimation of data with discontinuities," IEEE Transaction on Acoustics, Speech and Signal Processing, v.38, no.11 (November 1990), pp. 1972-1980.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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