Gauss-Markov measure field models for low-level vision
Marroquin, J.L.
Velasco, F.A.
Rivera, M.
Nakamura, M.
Centro de Investigacion en Matematicas, Guanajuato ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 2001
Volume: 23,
Issue: 4
On page(s): 337-348
ISSN: 0162-8828
References Cited: 31
CODEN: ITPIDJ
INSPEC Accession Number: 6921861
Digital Object Identifier: 10.1109/34.917570
Current Version Published: 2002-08-07
Abstract
We present a class of models, derived from classical discrete
Markov random fields, that may be used for the solution of ill-posed
problems in image processing and in computational vision. They lead to
reconstruction algorithms that are flexible, computationally efficient,
and biologically plausible. To illustrate their use, we present their
application to the reconstruction of the dominant orientation and
direction fields, to the classification of multiband images, and to
image quantization and filtering
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