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Proceedings Paper

Hierarchical Markov random field models applied to image analysis: a review
Author(s): Christine Graffigne; Fabrice Heitz; Patrick Perez; Francoise J. Preteux; Marc Sigelle; Josiane B. Zerubia
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Paper Abstract

The need for hierarchical statistical tools for modeling and processing image data, as well as the success of Markov random fields (MRFs) in image processing, have recently given rise to a significant research activity on hierarchical MRFs and their application to image analysis problems. Important contributions, relying on different models and optimization procedures, have thus been recorded in the literature. This paper presents a synthetic overview of available models and algorithms, as well as an attempt to clarify the vocabulary in this field. We propose to classify hierarchical MRF-based approaches as explicit and implicit methods, with appropriate subclasses. Each of these major classes is defined in the paper, and several specific examples of each class of approach are described.

Paper Details

Date Published: 11 August 1995
PDF: 16 pages
Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); doi: 10.1117/12.216341
Show Author Affiliations
Christine Graffigne, Univ. de Paris V--Univ. de Rene Descartes (France)
Fabrice Heitz, Ecole Nationale Superieure de Physique (France)
Patrick Perez, Institut de Recherche en Informatique at Systems Aleatoires (France)
Francoise J. Preteux, Institut National des Telecommunications (France)
Marc Sigelle, Ecole Nationale Superieure des Telecommunications (France)
Josiane B. Zerubia, INRIA-Sophia Antipolis (France)


Published in SPIE Proceedings Vol. 2568:
Neural, Morphological, and Stochastic Methods in Image and Signal Processing
Edward R. Dougherty; Francoise J. Preteux; Sylvia S. Shen, Editor(s)

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