Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem...
In this paper, we design a novel MRF framework which is called Non-Local Range Markov Random Field (NLRMRF). The local spatial range of clique in traditional MRF is extended to th...
In this paper we propose novel algorithms for image restoration and parameter estimation with a Generalized Gaussian Markov Random Field prior utilizing variational distribution a...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...