Gauss-Markov measure field models for low-level vision

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...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 23; no. 4; pp. 337 - 348
Main Authors Marroquin, J.L., Velasco, F.A., Rivera, M., Nakamura, M.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Biological system modeling
Biology computing
Classification
Computational efficiency
Computer vision
Distributed computing
Estimation theory
Filtering
Gaussian processes
Image reconstruction
Lattices
Markov random fields
Mathematical models
Pattern analysis
Reconstruction
State-space methods
Vision
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Summary: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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ISSN:0162-8828
1939-3539
DOI:10.1109/34.917570