Hierarchical Markov random field models applied to image analysis: a review

Christine Graffigne; Fabrice Heitz; Patrick Perez; Francoise J. Preteux; Marc Sigelle; Josiane B. Zerubia

doi:10.1117/12.216341

11 August 1995 Hierarchical Markov random field models applied to image analysis: a review

Christine Graffigne, Fabrice Heitz, Patrick Perez, Francoise J. Preteux, Marc Sigelle, Josiane B. Zerubia

Proceedings Volume 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing; (1995) https://doi.org/10.1117/12.216341
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

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.

Citation Download Citation

Christine Graffigne, Fabrice Heitz, Patrick Perez, Francoise J. Preteux, Marc Sigelle, and Josiane B. Zerubia "Hierarchical Markov random field models applied to image analysis: a review", Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); https://doi.org/10.1117/12.216341

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