By Topic

Hidden semi-Markov event sequence models: application to brain functional MRI sequence analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
S. Faisan ; LSIIT, CNRS, Illkirch, France ; L. Thoraval ; J. -P. Armspach ; F. Heitz

Due to the piecewise stationarity assumption required for the observable process of a hidden Markov chain, the application of hidden Markov models (HMMs) to the analysis of event-based random processes remains intricate. For such processes, a new class of HMMs is proposed: the hidden semi-Markov event sequence model (HSMESM). In a HSMESM, the observable process is no more considered as segmental in nature but issued from a detection-characterization preprocessing step. The standard markovian formalism is adapted accordingly. Results obtained in functional MRI sequence analysis validate this novel statistical modeling approach while opening new perspectives in detection-recognition of event-based random processes.

Published in:

Image Processing. 2002. Proceedings. 2002 International Conference on  (Volume:1 )

Date of Conference: