—We propose a general synchronous model of lattice random fields which could be used similarly to Gibbs distributions in a Bayesian framework for image analysis, leading to algor...
Conditional Random Fields (CRFs) are a state-of-the-art approach to natural language processing tasks like grapheme-tophoneme (g2p) conversion which is used to produce pronunciati...
Patrick Lehnen, Stefan Hahn, Andreas Guta, Hermann...
In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First,...
Many diagrams contain compound objects composed of parts. We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compou...
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...