Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A SurveyReport as inadecuate




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* Corresponding author 1 CVN - Centre de vision numérique 2 Perceiving Systems department 3 ENPC - École des Ponts ParisTech 4 LIGM - Laboratoire d-Informatique Gaspard-Monge 5 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content Inria Saclay - Ile de France, Ecole Centrale Paris

Abstract : In this paper, we present a comprehensive survey of Markov Random Fields MRFs in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.

Keywords : Markov Random Fields Graphical Models MRFs MAP Inference Discrete Optimization MRF Learning





Author: Chaohui Wang - Nikos Komodakis - Nikos Paragios -

Source: https://hal.archives-ouvertes.fr/



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