Title :
Markov and recursive least squares methods for the estimation of data with discontinuities
Author_Institution :
Dept. of Electr. & Comput. Eng., US Naval Postgraduate Sch., Monterey, CA, USA
fDate :
11/1/1990 12:00:00 AM
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;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
