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The Publications
Result of the query in the list of publications :
101 Articles |
81 - Globally optimal regions and boundaries as minimum ratio weight cycles.
I. H. Jermyn and H. Ishikawa. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(10): pages 1075-1088, October 2001.
Keywords : Graph, Ratio, Cycle, Segmentation, Global minimum.
Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@ARTICLE{jermyn_tpami01,
|
| author |
= |
{Jermyn, I. H. and Ishikawa, H.}, |
| title |
= |
{Globally optimal regions and boundaries as minimum ratio weight cycles}, |
| year |
= |
{2001}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{23}, |
| number |
= |
{10}, |
| pages |
= |
{1075-1088}, |
| url |
= |
{http://dx.doi.org/10.1109/34.954599}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_tpami01.pdf}, |
| keyword |
= |
{Graph, Ratio, Cycle, Segmentation, Global minimum} |
| } |
Abstract :
We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,ldots), em and from the interior of the region (texture, homogeneity,ldots). We describe two polynomial-time digraph algorithms for finding the em global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 times 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization. |
|
82 - A RJMCMC algorithm for object processes in image processing.
X. Descombes and R. Stoica and L. Garcin and J. Zerubia. Monte Carlo Methods and Applications, 7(1-2): pages 149-156, 2001.
@ARTICLE{xd01c,
|
| author |
= |
{Descombes, X. and Stoica, R. and Garcin, L. and Zerubia, J.}, |
| title |
= |
{A RJMCMC algorithm for object processes in image processing}, |
| year |
= |
{2001}, |
| journal |
= |
{Monte Carlo Methods and Applications}, |
| volume |
= |
{7}, |
| number |
= |
{1-2}, |
| pages |
= |
{149-156}, |
| url |
= |
{http://www.degruyter.com/view/j/mcma.2001.7.issue-1-2/mcma.2001.7.1-2.149/mcma.2001.7.1-2.149.xml}, |
| keyword |
= |
{} |
| } |
|
83 - Image segmentation using Markov random field model in fully parallel cellular network architectures.
T. Szirányi and J. Zerubia and L. Czúni and D. Geldreich and Z. Kato. Real Time Imaging, 6(3): pages 195-211, June 2000.
@ARTICLE{jz00y,
|
| author |
= |
{Szirányi, T. and Zerubia, J. and Czúni, L. and Geldreich, D. and Kato, Z.}, |
| title |
= |
{Image segmentation using Markov random field model in fully parallel cellular network architectures}, |
| year |
= |
{2000}, |
| month |
= |
{June}, |
| journal |
= |
{Real Time Imaging}, |
| volume |
= |
{6}, |
| number |
= |
{3}, |
| pages |
= |
{195-211}, |
| pdf |
= |
{http://dx.doi.org/10.1006/rtim.1998.0159}, |
| keyword |
= |
{} |
| } |
Abstract :
Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imaging VLSI chips.
As an example, we have developed a simplified statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNN-UM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Metropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNN-UM. We can introduce the whole pseudo-stochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 μs.
In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested different monogrid and multigrid architectures.
In our CNN-UM model several complex preprocessing steps can be involved, such as texture-classification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a high-level image segmentation machine, using only simple functions based on the close-neighborhood of a pixel. |
|
84 - A variational model for image classification and restoration.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. IEEE Trans. Pattern Analysis ans Machine Intelligence, 22(5): pages 460-472, May 2000.
@ARTICLE{cs00,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{A variational model for image classification and restoration}, |
| year |
= |
{2000}, |
| month |
= |
{May}, |
| journal |
= |
{IEEE Trans. Pattern Analysis ans Machine Intelligence}, |
| volume |
= |
{22}, |
| number |
= |
{5}, |
| pages |
= |
{460-472}, |
| pdf |
= |
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=857003}, |
| keyword |
= |
{} |
| } |
|
85 - A Level Set Model for Image Classification.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. International Journal of Computer Vision, 40(3): pages 187-198, 2000.
@ARTICLE{cs00b,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{A Level Set Model for Image Classification}, |
| year |
= |
{2000}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{40}, |
| number |
= |
{3}, |
| pages |
= |
{187-198}, |
| url |
= |
{http://link.springer.com/article/10.1023%2FA%3A1008183109594}, |
| keyword |
= |
{} |
| } |
|
86 - Mise en correspondance et recalage de graphes~: application aux réseaux routiers extraits d'un couple carte/image.
C. Hivernat and X. Descombes and S. Randriamasy and J. Zerubia. Traitement du Signal, 17(1): pages 21-32, 2000.
@ARTICLE{xd00,
|
| author |
= |
{Hivernat, C. and Descombes, X. and Randriamasy, S. and Zerubia, J.}, |
| title |
= |
{Mise en correspondance et recalage de graphes~: application aux réseaux routiers extraits d'un couple carte/image}, |
| year |
= |
{2000}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{17}, |
| number |
= |
{1}, |
| pages |
= |
{21-32}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/2129}, |
| keyword |
= |
{} |
| } |
|
87 - Texture analysis through a Markovian modelling and fuzzy classification: Application to urban area Extraction from Satellite Images.
A. Lorette and X. Descombes and J. Zerubia. International Journal of Computer Vision, 36(3): pages 221-236, 2000.
@ARTICLE{xd00a,
|
| author |
= |
{Lorette, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Texture analysis through a Markovian modelling and fuzzy classification: Application to urban area Extraction from Satellite Images}, |
| year |
= |
{2000}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{36}, |
| number |
= |
{3}, |
| pages |
= |
{221-236}, |
| url |
= |
{http://dx.doi.org/10.1023/A:1008129103384}, |
| pdf |
= |
{http://dx.doi.org/10.1023/A:1008129103384}, |
| keyword |
= |
{} |
| } |
|
88 - Comparison of Filtering Methods for fMRI Datasets.
F. Kruggel and Y. Von Cramon and X. Descombes. NeuroImage, 10(5): pages 530-543, November 1999.
@ARTICLE{xd99d,
|
| author |
= |
{Kruggel, F. and Von Cramon, Y. and Descombes, X.}, |
| title |
= |
{Comparison of Filtering Methods for fMRI Datasets}, |
| year |
= |
{1999}, |
| month |
= |
{November}, |
| journal |
= |
{NeuroImage}, |
| volume |
= |
{10}, |
| number |
= |
{5}, |
| pages |
= |
{530-543}, |
| url |
= |
{http://www.sciencedirect.com/science/article/pii/S1053811999904901}, |
| keyword |
= |
{} |
| } |
|
89 - Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours.
L. Blanc-Féraud and G. Aubert. International Journal of Computer Vision, 34(1): pages 19-28, September 1999.
@ARTICLE{lbf99a,
|
| author |
= |
{Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{34}, |
| number |
= |
{1}, |
| pages |
= |
{19-28}, |
| url |
= |
{http://link.springer.com/article/10.1023%2FA%3A1008168219878}, |
| keyword |
= |
{} |
| } |
|
90 - Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood.
X. Descombes and R. Morris and J. Zerubia and M. Berthod. IEEE Trans. Image Processing, 8(7): pages 954-963, July 1999.
Keywords : Markov processes, Monte Carlo methods, Potts model, Image segmentation, Maximum likelihood estimation .
@ARTICLE{xd99c,
|
| author |
= |
{Descombes, X. and Morris, R. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{8}, |
| number |
= |
{7}, |
| pages |
= |
{954-963}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=16772&arnumber=772239&count=14&index=6}, |
| keyword |
= |
{Markov processes, Monte Carlo methods, Potts model, Image segmentation, Maximum likelihood estimation } |
| } |
Abstract :
Developments in statistics now allow maximum likelihood estimators for the parameters of Markov random fields (MRFs) to be constructed. We detail the theory required, and present an algorithm that is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models-the standard Potts model, an inhomogeneous variation of the Potts model, and a long-range interaction model, better adapted to modeling real-world images. We estimate the parameters from a synthetic and a real image, and then resynthesize the models to demonstrate which features of the image have been captured by the model. Segmentations are computed based on the estimated parameters and conclusions drawn. |
|
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} |
| } |
|
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