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