
The Publications
Result of the query in the list of publications :
101 Articles 
91  A Markov Pixon Information approach for low level image description. X. Descombes and F. Kruggel. IEEE Trans. Pattern Analysis ans Machine Intelligence, 21(6): pages 482494, June 1999.
@ARTICLE{xd99b,

author 
= 
{Descombes, X. and Kruggel, F.}, 
title 
= 
{A Markov Pixon Information approach for low level image description}, 
year 
= 
{1999}, 
month 
= 
{June}, 
journal 
= 
{IEEE Trans. Pattern Analysis ans Machine Intelligence}, 
volume 
= 
{21}, 
number 
= 
{6}, 
pages 
= 
{482494}, 
pdf 
= 
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=771311}, 
keyword 
= 
{} 
} 

92  Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline. T. Corbard and L. BlancFéraud and G. Berthomieu and J. Provost. Astronomy and Astrophysics, (344): pages 696708, 1999.
@ARTICLE{lbf99b,

author 
= 
{Corbard, T. and BlancFéraud, L. and Berthomieu, G. and Provost, J.}, 
title 
= 
{Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline}, 
year 
= 
{1999}, 
journal 
= 
{Astronomy and Astrophysics}, 
number 
= 
{344}, 
pages 
= 
{696708}, 
url 
= 
{http://arxiv.org/abs/astroph/9901112}, 
keyword 
= 
{} 
} 

93  GMRF Parameter Estimation in a nonstationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging. X. Descombes and M. Sigelle and F. Prêteux. IEEE Trans. Image Processing, 8(4): pages 490503, 1999.
@ARTICLE{xd99a,

author 
= 
{Descombes, X. and Sigelle, M. and Prêteux, F.}, 
title 
= 
{GMRF Parameter Estimation in a nonstationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging}, 
year 
= 
{1999}, 
journal 
= 
{IEEE Trans. Image Processing}, 
volume 
= 
{8}, 
number 
= 
{4}, 
pages 
= 
{490503}, 
url 
= 
{https://hal.archivesouvertes.fr/hal00272393}, 
keyword 
= 
{} 
} 

94  Unsupervised parallel image classification using Markovian models. Z. Kato and J. Zerubia and M. Berthod. Pattern Recognition, 32(4): pages 591604, 1999. Keywords : Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification.
@ARTICLE{jz99a,

author 
= 
{Kato, Z. and Zerubia, J. and Berthod, M.}, 
title 
= 
{Unsupervised parallel image classification using Markovian models}, 
year 
= 
{1999}, 
journal 
= 
{Pattern Recognition}, 
volume 
= 
{32}, 
number 
= 
{4}, 
pages 
= 
{591604}, 
pdf 
= 
{http://dx.doi.org/10.1016/S00313203(98)001046}, 
keyword 
= 
{Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification} 
} 
Abstract :
This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation–maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images. 

95  Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm. A. Strandlie and J. Zerubia. Computer Physics Communications, 123(13): pages 7787, 1999.
@ARTICLE{jz99b,

author 
= 
{Strandlie, A. and Zerubia, J.}, 
title 
= 
{Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm}, 
year 
= 
{1999}, 
journal 
= 
{Computer Physics Communications}, 
volume 
= 
{123}, 
number 
= 
{13}, 
pages 
= 
{7787}, 
url 
= 
{http://www.sciencedirect.com/science/article/pii/S0010465599002581}, 
keyword 
= 
{} 
} 

96  A generalized sampling theory without bandlimiting constraints. M. Unser and J. Zerubia. IEEE Trans. on Circuits And Systems II, 45(8): pages 959969, August 1998.
@ARTICLE{jz98b,

author 
= 
{Unser, M. and Zerubia, J.}, 
title 
= 
{A generalized sampling theory without bandlimiting constraints}, 
year 
= 
{1998}, 
month 
= 
{August}, 
journal 
= 
{IEEE Trans. on Circuits And Systems II}, 
volume 
= 
{45}, 
number 
= 
{8}, 
pages 
= 
{959969}, 
pdf 
= 
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=718806}, 
keyword 
= 
{} 
} 

97  Variational approach for edge preserving regularization using coupled PDE's. S. Teboul and L. BlancFéraud and G. Aubert and M. Barlaud. IEEE Trans. Image Processing, 7(3): pages 387397, March 1998.
@ARTICLE{lbf98,

author 
= 
{Teboul, S. and BlancFéraud, L. and Aubert, G. and Barlaud, M.}, 
title 
= 
{Variational approach for edge preserving regularization using coupled PDE's}, 
year 
= 
{1998}, 
month 
= 
{March}, 
journal 
= 
{IEEE Trans. Image Processing}, 
volume 
= 
{7}, 
number 
= 
{3}, 
pages 
= 
{387397}, 
pdf 
= 
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=661189}, 
keyword 
= 
{} 
} 

98  Combined constraints for efficient algebraic regularized methods. I. Laurette and J. Darcourt and L. BlancFéraud and P.M. Koulibaly and M. Barlaud. Physics in Medicine and Biology, 43(4): pages 9911000, 1998.
@ARTICLE{lbf98a,

author 
= 
{Laurette, I. and Darcourt, J. and BlancFéraud, L. and Koulibaly, P.M. and Barlaud, M.}, 
title 
= 
{Combined constraints for efficient algebraic regularized methods}, 
year 
= 
{1998}, 
journal 
= 
{Physics in Medicine and Biology}, 
volume 
= 
{43}, 
number 
= 
{4}, 
pages 
= 
{9911000}, 
url 
= 
{http://iopscience.iop.org/00319155/43/4/026}, 
keyword 
= 
{} 
} 

99  fMRI Signal Restoration Using an Edge Preserving Spatiotemporal Markov Random Field. X. Descombes and F. Kruggel and Y. von Cramon. NeuroImage, 8: pages 340349, 1998. Keywords : fMRI, Restoration, Markov Fields. Copyright : published in NeuroIMage by Elsevier
http://www.elsevier.com/wps/find/homepage.cws_home
@ARTICLE{descombes98d,

author 
= 
{Descombes, X. and Kruggel, F. and von Cramon, Y.}, 
title 
= 
{fMRI Signal Restoration Using an Edge Preserving Spatiotemporal Markov Random Field}, 
year 
= 
{1998}, 
journal 
= 
{NeuroImage}, 
volume 
= 
{8}, 
pages 
= 
{340349}, 
pdf 
= 
{ftp://ftpsop.inria.fr/ariana/Articles/1998_descombes98d.pdf}, 
keyword 
= 
{fMRI, Restoration, Markov Fields} 
} 

100  Spatiotemporal fMRI analysis using Markov Random Fields. X. Descombes and F. Kruggel and Y. Von Cramon. IEEE Trans. Medical Imaging, 17(6): pages 10281039, 1998. Note : to appear. Keywords : fMRI, Markov Random Fields.
@ARTICLE{descombes98,

author 
= 
{Descombes, X. and Kruggel, F. and Von Cramon, Y.}, 
title 
= 
{Spatiotemporal fMRI analysis using Markov Random Fields}, 
year 
= 
{1998}, 
journal 
= 
{IEEE Trans. Medical Imaging}, 
volume 
= 
{17}, 
number 
= 
{6}, 
pages 
= 
{10281039}, 
pdf 
= 
{http://wwwsop.inria.fr/members/Xavier.Descombes/publis_dr/TMI1.pdf}, 
keyword 
= 
{fMRI, Markov Random Fields} 
} 

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