Markov Random Fields and Images
Patrick Pérez, 35 - Rennes (France). Inst. de Recherche en Informatique et Systemes Aleatoires (IRISA) Centre National de la Recherche Scientifique (CNRS), 35 (France). Inst. de Recherche en Informatique et Systemes Aleatoires (IRISA) Rennes-1 Univ., 35 (France). Inst. de Recherche en Informatique et Systemes Aleatoires (IRISA) Institut National des Sciences Appliquees de Rennes (INSA), 35 - Rennes (France). Inst. de Recherche en Informatique et Systemes Aleatoires (IRISA) Institut National de Recherche en Informatique et en Automatique (INRIA) · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · 1998
At the intersection of statistical physics and probability theory, Markov random elds and Gibbs distributions have emerged in the early eighties as powerful tools for modeling images and coping with high-dimensional inverse problems from lowlevel vision. Since then, they have been used in many studies from the image processing and computer vision community. Abrief and simple introduction to the basics of the domain is proposed. 1. Introduction and