Markov Random Fields in Pattern Recognition for Semiconductor Manufacturing

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

Industrial forecasting is a top-echelon research domain, which has over the past several years experienced highly provocative research discussions. The scope of this research domain continues to expand due to the continuous knowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on current research issues in the accident domain will potentially ...

Guest Editors' Introduction to the Special Section on Probabilistic Graphical Models

An exciting development over the last decade has been the gradually widespread adoption of probabilistic graphical models (PGMs) in many areas of computer vision and pattern recognition. Representing an integration of graph and probability theories, a number of families of graphical models have been studied in the statistics and machine learning literatures. Examples of directed graphical model...

Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models

Image registration using Markov random coefficient and geometric transformation fields

Image Registration is central to different applications such as medical analysis, biomedical systems, image guidance, etc. In this paper we propose a new algorithm for multimodal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on coefficient and geometric fields. These coefficients, that represent the local intensity...

Direct estimate of motion parameters by means of Markov random fields

Motion estimation in image sequences is undoubtedly one of the most studied problems because for many applications, going from video coding to pattern recognition, motion estimation is a fundamental tool. In this paper a new methodology which, by minimizing a specific potential function, determines for each image pixel its motion parameter set is presented. The approach is based on MRFs (Markov...