This document discusses using hidden Markov random field models for railway infrastructure diagnosis. It presents an application of HMRF models to integrate data from eddy current sensors for detecting major and minor rail defects. The document is outlined with sections on the general context, track setting rules learning, sensor data integration, methodology, results, and conclusions.

1. Hidden Markov Random Field, an application to railway
infrastructure diagnosis
E. Cˆme
o L. Bouillaut P. Aknin L. Oukhellou
etienne.come@inrets.fr
French National Institut for Transport and Safety Research
Laboratory of new technologies
http://www.inrets.fr/ur/ltn/polediag/pole diag.html
Etienne Cˆme ()
o DCDS 2007 28 juin 2007 1 / 18

2. Outline
Outline
General Context
1
Track setting rules Learning
2
Sensor data integration
3
Methodology
4
Results
5
Conlusion
6
Bibliography
7
Etienne Cˆme ()
o DCDS 2007 28 juin 2007 2 / 18