The max-sum labeling problem, defined as maximizing a sum of binary (ie, pairwise)
functions of discrete variables, is a general NP-hard optimization problem with many …

In this paper we introduce a novel method to address minimization of static and dynamic
MRFs. Our approach is based on principles from linear programming and, in particular, on …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Clustering data by identifying a subset of representative examples is important for
processing sensory signals and detecting patterns in data. Such “exemplars” can be found …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

Tree-reweighted max-product message passing (TRW) is an algorithm for energy
minimization introduced recently by Wainwright et al.[7]. It shares some similarities with …

Among the most exciting advances in early vision has been the development of efficient
energy minimization algorithms for pixel-labeling tasks such as depth or texture …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …

Semantic segmentation is a crucial image understanding task, where each pixel of image is
categorized into a corresponding label. Since the pixel-wise labeling for ground-truth is …