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Proceedings Paper

Adaptive quantization and filtering using Gauss-Markov measure field models
Author(s): Jose Luis Marroquin Zaleta; Salvador Botello; Mariano Rivera
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Paper Abstract

We present a new class of models, derived form classical Markov Random Fields, that may be used for the solution of ill-posed problems in image processing and computational vision. They lead to reconstruction algorithms that are flexible, computationally efficient and biological plausible. To illustrate their use, we present their application to the reconstruction of the dominant orientation field and to the adaptive quantization and filtering of images in a variety of situations.

Paper Details

Date Published: 22 September 1998
PDF: 12 pages
Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323803
Show Author Affiliations
Jose Luis Marroquin Zaleta, Ctr. de Investigacion en Matematicas (Mexico)
Salvador Botello, Ctr. de Investigacion en Matematicas (Mexico)
Mariano Rivera, Ctr. de Investigacion en Matematicas (Mexico)

Published in SPIE Proceedings Vol. 3459:
Bayesian Inference for Inverse Problems
Ali Mohammad-Djafari, Editor(s)

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