|
|
Publications of type 'article'
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 482-494, 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 |
= |
{482-494}, |
| 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. Blanc-Féraud and G. Berthomieu and J. Provost. Astronomy and Astrophysics, (344): pages 696-708, 1999.
@ARTICLE{lbf99b,
|
| author |
= |
{Corbard, T. and Blanc-Féraud, 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 |
= |
{696-708}, |
| url |
= |
{http://arxiv.org/abs/astro-ph/9901112}, |
| keyword |
= |
{} |
| } |
|
93 - GMRF Parameter Estimation in a non-stationary 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 490-503, 1999.
@ARTICLE{xd99a,
|
| author |
= |
{Descombes, X. and Sigelle, M. and Prêteux, F.}, |
| title |
= |
{GMRF Parameter Estimation in a non-stationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging}, |
| year |
= |
{1999}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{8}, |
| number |
= |
{4}, |
| pages |
= |
{490-503}, |
| url |
= |
{https://hal.archives-ouvertes.fr/hal-00272393}, |
| keyword |
= |
{} |
| } |
|
94 - Unsupervised parallel image classification using Markovian models.
Z. Kato and J. Zerubia and M. Berthod. Pattern Recognition, 32(4): pages 591-604, 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 |
= |
{591-604}, |
| pdf |
= |
{http://dx.doi.org/10.1016/S0031-3203(98)00104-6}, |
| 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(1-3): pages 77-87, 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 |
= |
{1-3}, |
| pages |
= |
{77-87}, |
| 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 959-969, 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 |
= |
{959-969}, |
| 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. Blanc-Féraud and G. Aubert and M. Barlaud. IEEE Trans. Image Processing, 7(3): pages 387-397, March 1998.
@ARTICLE{lbf98,
|
| author |
= |
{Teboul, S. and Blanc-Féraud, 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 |
= |
{387-397}, |
| 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. Blanc-Féraud and P.M. Koulibaly and M. Barlaud. Physics in Medicine and Biology, 43(4): pages 991-1000, 1998.
@ARTICLE{lbf98a,
|
| author |
= |
{Laurette, I. and Darcourt, J. and Blanc-Féraud, 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 |
= |
{991-1000}, |
| url |
= |
{http://iopscience.iop.org/0031-9155/43/4/026}, |
| keyword |
= |
{} |
| } |
|
99 - fMRI Signal Restoration Using an Edge Preserving Spatio-temporal Markov Random Field.
X. Descombes and F. Kruggel and Y. von Cramon. NeuroImage, 8: pages 340-349, 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 Spatio-temporal Markov Random Field}, |
| year |
= |
{1998}, |
| journal |
= |
{NeuroImage}, |
| volume |
= |
{8}, |
| pages |
= |
{340-349}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/1998_descombes98d.pdf}, |
| keyword |
= |
{fMRI, Restoration, Markov Fields} |
| } |
|
100 - Spatio-temporal fMRI analysis using Markov Random Fields.
X. Descombes and F. Kruggel and Y. Von Cramon. IEEE Trans. Medical Imaging, 17(6): pages 1028-1039, 1998.
Note : to appear.
Keywords : fMRI, Markov Random Fields.
@ARTICLE{descombes98,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Von Cramon, Y.}, |
| title |
= |
{Spatio-temporal fMRI analysis using Markov Random Fields}, |
| year |
= |
{1998}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{17}, |
| number |
= |
{6}, |
| pages |
= |
{1028-1039}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Xavier.Descombes/publis_dr/TMI1.pdf}, |
| keyword |
= |
{fMRI, Markov Random Fields} |
| } |
|
 top of the page
These pages were generated by 
|
2011 Activity report
get the bibtex
