|
|
Publications of 1999
Result of the query in the list of publications :
8 Articles |
1 - 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 |
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{5}, |
| pages |
= |
{530-543}, |
| url |
= |
{http://www.sciencedirect.com/science/article/pii/S1053811999904901}, |
| keyword |
= |
{} |
| } |
|
2 - 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 |
= |
{} |
| } |
|
3 - 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. |
|
4 - 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 |
= |
{} |
| } |
|
5 - 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 |
= |
{} |
| } |
|
6 - 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 |
= |
{} |
| } |
|
7 - 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. |
|
8 - 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 |
= |
{} |
| } |
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PhD Thesis and Habilitation |
1 - Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales.
A. Lorette. PhD Thesis, Universite de Nice Sophia Antipolis, September 1999.
Keywords : Texture, Segmentation, Markov Fields, Mathematical morphology, Urban areas.
@PHDTHESIS{lorette99,
|
| author |
= |
{Lorette, A.}, |
| title |
= |
{Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{Theses/these-lorette.pdf}, |
| keyword |
= |
{Texture, Segmentation, Markov Fields, Mathematical morphology, Urban areas} |
| } |
Résumé :
Dans cette thèse, nous nous intéressons au problème de l'analyse urbaine à partir d'images satellitales par des méthodes automatiques ou semi-automatiques issues du traitement d'image. Dans le premier chapitre, nous présentons le contexte dans lequel le travail a été effectué. Nous exposons les types de données utilisées, les approches statistiques considérées. Nous donnons également quelques exemples d'applications qui justifient une telle étude. Enfin, un état de l'art des diverses méthodes d'analyse des textures est présenté. Dans les deux chapitres suivants, nous développons une méthode automatique d'extraction d'un masque urbain à partir d'une analyse de la texture de l'image. Des méthodes d'extraction d'un masque urbain sont décrites. Ensuite, nous définissons plus précisemment les huit modèles markoviens gaussiens fondés sur des chaines. Ces modèles sont renormalisés par une méthode de renormalisation de groupe issue de la physique statistique afin de corriger le biais introduit par l'anisotropie du réseau de pixels. L'analyse de texture proposée est comparée avec deux méthodes classiques: les matrices de cooccurrence et les filtres de Gabor. L'image du paramètre de texture est ensuite classifiée avec un algorithme non supervisé de classification floue fondée sur la définition d'un critère entropique. Les paramètres estimés avec cet algorithme sont intégrés dans un modèle markovien de segmentation. Des résultats d'extraction de masques urbains sont finalement présentés sur des images satellitales optiques SPOT3, des simulations SPOT5, et des images radar ERS1. Dans le quatrième chapitre, nous présentons l'analyse granulométrique utilisée pour analyser le paysage urbain. Les outils et définitions de base de la morphologie mathématique sont exposés. Nous nous intéressons plus particulièrement à l'ouverture par reconstruction qui est utilisée comme transformation de base de la granulométrie. L'étape de quantification qui suit tout étape de transformation nous permet d'estimer en chaque pixel une distribution locale de taille qui est intégrée dans le terme d'attache aux données d'un modèle markovien de segmentation. Des tests sont effectués sur des simulations SPOT5. |
Abstract :
In this thesis, we investigate the problem of urban areas analysis from satellite images by automatic or semi-automatic methods coming from image processing. In the first chapter, we describe the context of this work, i.e. the type of used data, the statistical applied methods. We also give some examples of the applications which require such an analysis. Finally, a study of the existing methods of texture analysis is presented. In the second and third chapter, we develop a non supervised method based on texture analysis in order to extract an urban mask. First a study of the existing methods of urban mask extraction is presented. Second we precisely describe the eight chain-based Gaussian Markovian models used to characterize urban texture. These models are normalized through a renormalization group technique derived from statistical physics in order to correct the bias introduced by the anisotropy of the lattice.The above mentionned method of texture analysis is then compared with two classical ones: coocurrences matrix and Gabor filters. The image is then partitionned by an unsupervised fuzzy Cmeans algorithm based on an entropic criterion. The final segmentation is performed by the minimization of an energy derived from a Markovian model. Some results are presented that are obtained from SPOT3 images, SPOT5 simulations and radar ERS1 images. In the fourth chapter, we present the granulometric approach used to segment within the urban area itself. The basic operations and definitions of mathematical morphology are settled. We are particularly interested in opening by reconstruction operation based on geodesic dilatations. In fact this operation is used to define a granulometry. The quantification step that follows the transformation step consists in estimating a local size distribution function for each pixel. These parameters are then integrated in the data term of a Markovian model. Some results on SPOT5 simulations are presented. |
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14 Conference articles |
1 - Two Markov point processes for simulating line networks.
X. Descombes and R. Stoica and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Kobe, Japon, October 1999.
@INPROCEEDINGS{xd99g,
|
| author |
= |
{Descombes, X. and Stoica, R. and Zerubia, J.}, |
| title |
= |
{Two Markov point processes for simulating line networks}, |
| year |
= |
{1999}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Kobe, Japon}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=822850}, |
| keyword |
= |
{} |
| } |
|
2 - Texture Analysis through Markov Random Fields: Urban Areas Extractions.
A. Lorette and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Kobe, Japon, October 1999.
@INPROCEEDINGS{xd99j,
|
| author |
= |
{Lorette, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Texture Analysis through Markov Random Fields: Urban Areas Extractions}, |
| year |
= |
{1999}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Kobe, Japon}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=819629}, |
| keyword |
= |
{} |
| } |
|
3 - Isotropic properties of some multi-body interaction models.
X. Descombes and E. Pechersky. In Proc. Fundamental Structural Properties in Image and Pattern Analysis, Budapest, Hongrie, September 1999.
@INPROCEEDINGS{xd99f,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Isotropic properties of some multi-body interaction models}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Fundamental Structural Properties in Image and Pattern Analysis}, |
| address |
= |
{Budapest, Hongrie}, |
| url |
= |
{http://www.ercim.eu/publication/Ercim_News/enw40/chetverikov.html}, |
| keyword |
= |
{} |
| } |
|
4 - Restauration automatique d'images satellitaires par une méthode MCMC.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Vannes, France, September 1999.
@INPROCEEDINGS{aj99c,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Restauration automatique d'images satellitaires par une méthode MCMC}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Vannes, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/12942}, |
| keyword |
= |
{} |
| } |
|
5 - A Level Set Model for Image Classification.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Scale-Space, Corfu, Grèce, September 1999.
@INPROCEEDINGS{cs99d,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{A Level Set Model for Image Classification}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Scale-Space}, |
| address |
= |
{Corfu, Grèce}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-48236-9_27}, |
| keyword |
= |
{} |
| } |
|
6 - Metropolis vs Kawasaki dynamic for image segmentation based on Gibbs models.
X. Descombes and E. Pechersky. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), York, UK, July 1999.
@INPROCEEDINGS{xd99e,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Metropolis vs Kawasaki dynamic for image segmentation based on Gibbs models}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{York, UK}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-48432-9_8}, |
| keyword |
= |
{} |
| } |
|
7 - Graph-matching model using Gibbsian modeling : application to map-SPOT image road networks for map updating.
X. Descombes and C. Hivernat and S. Randriamasy and J. Zerubia. In Proc. SPIE Conference on Wavelets, Denver, USA, July 1999.
@INPROCEEDINGS{xd99i,
|
| author |
= |
{Descombes, X. and Hivernat, C. and Randriamasy, S. and Zerubia, J.}, |
| title |
= |
{Graph-matching model using Gibbsian modeling : application to map-SPOT image road networks for map updating}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. SPIE Conference on Wavelets}, |
| address |
= |
{Denver, USA}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=906165}, |
| keyword |
= |
{} |
| } |
|
8 - Hyperparameter estimation for satellite image restoration by a MCMCML method.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), York,UK, July 1999.
@INPROCEEDINGS{aj99b,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Hyperparameter estimation for satellite image restoration by a MCMCML method}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{York,UK}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-48432-9_9}, |
| keyword |
= |
{} |
| } |
|
9 - Simultaneous Image Classification and Restoration Using a Variational Approach.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Fort Collins, Colorado, USA, June 1999.
@INPROCEEDINGS{cs99c,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Simultaneous Image Classification and Restoration Using a Variational Approach}, |
| year |
= |
{1999}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Fort Collins, Colorado, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=784985}, |
| keyword |
= |
{} |
| } |
|
10 - Déconvolution d'images satellitaires: modèles et estimation de paramètres.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), Hammamet, Tunisie, March 1999.
@INPROCEEDINGS{aj99a,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Déconvolution d'images satellitaires: modèles et estimation de paramètres}, |
| year |
= |
{1999}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, |
| address |
= |
{Hammamet, Tunisie}, |
| pdf |
= |
{Articles/taima99.pdf}, |
| keyword |
= |
{} |
| } |
|
11 - Auxiliary functions and optimal scanning for road detection by dynamic programming.
N. Merlet and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, USA, March 1999.
@INPROCEEDINGS{jz99d,
|
| author |
= |
{Merlet, N. and Zerubia, J.}, |
| title |
= |
{Auxiliary functions and optimal scanning for road detection by dynamic programming}, |
| year |
= |
{1999}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Phoenix, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=757554}, |
| keyword |
= |
{} |
| } |
|
12 - Qualification automatique des résultats d'une mise en correspondance de réseaux routiers en vue de la mise à jour cartographique.
C. Hivernat and S. Randriamasy and X. Descombes and J. Zerubia. In Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Paris, France, 1999.
@INPROCEEDINGS{xd99h,
|
| author |
= |
{Hivernat, C. and Randriamasy, S. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Qualification automatique des résultats d'une mise en correspondance de réseaux routiers en vue de la mise à jour cartographique}, |
| year |
= |
{1999}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing (ISPRS)}, |
| address |
= |
{Paris, France}, |
| keyword |
= |
{} |
| } |
|
13 - Classification et Restauration d'Images par Approche Variationnelle.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Aussois, France, 1999.
@INPROCEEDINGS{cs99b,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Classification et Restauration d'Images par Approche Variationnelle}, |
| year |
= |
{1999}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Aussois, France}, |
| pdf |
= |
{Articles/orasis1999.pdf}, |
| keyword |
= |
{} |
| } |
|
14 - Globally optimal regions and boundaries.
I. H. Jermyn and H. Ishikawa. In Proc. IEEE International Conference on Computer Vision (ICCV), 1999.
Keywords : global, optimum, Graph, Cycle, Ratio, Segmentation.
Copyright :
@INPROCEEDINGS{Jermyn99iccv,
|
| author |
= |
{Jermyn, I. H. and Ishikawa, H.}, |
| title |
= |
{Globally optimal regions and boundaries}, |
| year |
= |
{1999}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jermyn99iccv.pdf}, |
| keyword |
= |
{global, optimum, Graph, Cycle, Ratio, Segmentation} |
| } |
Abstract :
We propose a new form of energy functional for the segmentation
of regions in images, and an efficient method for
finding its global optima. The energy can have contributions
from both the region and its boundary, thus combining
the best features of region- and boundary-based approaches
to segmentation. By transforming the region energy
into a boundary energy, we can treat both contributions
on an equal footing, and solve the global optimization
problem as a minimum mean weight cycle problem on
a directed graph. The simple, polynomial-time algorithm
requires no initialization and is highly parallelizable. |
|
top of the page
3 Technical and Research Reports |
1 - Isotropic Properties of Some Multi-body Interaction Models: Two Quality Criteria for Markov Priors in Image Processing.
X. Descombes and E. Pechersky. Research Report 3752, Inria, August 1999.
Keywords : Gibbs Random Fields, Segmentation, Restoration.
@TECHREPORT{xd99k,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Isotropic Properties of Some Multi-body Interaction Models: Two Quality Criteria for Markov Priors in Image Processing}, |
| year |
= |
{1999}, |
| month |
= |
{August}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3752}, |
| url |
= |
{http://hal.inria.fr/inria-00072910}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/07/29/10/PDF/RR-3752.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/29/10/PS/RR-3752.ps}, |
| keyword |
= |
{Gibbs Random Fields, Segmentation, Restoration} |
| } |
Résumé :
Les champs de Gibbs sont très utilisés en traitement d'image à la fois pour la segmentation et la restauration. Définis sur la trâme discrète sous-jacente à l'image, ils présentent un comportement non isotrope. Dans ce rapport, nous étudions et quantifions cette non-isotropie, pour des modèles avec des interactions 3x3, en calculant la tension de bord en fonction de l'angle d'une droite séparant le plan en deux parties contenant une phase différente. De cette étude, nous dérivons deux critères quantitatifs d'anisotropie des modèles. Nous calculons ensuite la forme d'une goutte d'une phase immergée dans une autre phase à la température nulle pour les différents modèles, et étudions la non isotropie des formes obtenues. Pour finir, les artéfacts induits par cette non-isotropie sont mis en évidence sur des exemples de segmentation et de restauration d'image. |
Abstract :
Gibbs Fields are widely used in image processing for both segmentation and restoration. Defined on a discrete lattice representing the image they exhibit a non-isotropic behavior. Herein, we study and quantify this non-isotropy by computing the boundary tension as a function of the angle of a line separating the plane in two parts containing a different phase. From this study, we derive two quantitative criteria of the non isotropy of the model. We then compute the shape at zero temperature of a droplet of one phase within the other phase and study the non-isotropy of the shape for the different models. Finally, we show the artifacts due to this non-isotropic behavior for image segmentation and restoration. |
|
2 - A Deterministic Annealing PMHT Algorithm with an Application to Particle Tracking.
A. Strandlie and J. Zerubia. Research Report 3711, Inria, June 1999.
Keywords : EM algorithm, Particle tracking.
@TECHREPORT{jz99c,
|
| author |
= |
{Strandlie, A. and Zerubia, J.}, |
| title |
= |
{A Deterministic Annealing PMHT Algorithm with an Application to Particle Tracking}, |
| year |
= |
{1999}, |
| month |
= |
{June}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3711}, |
| url |
= |
{https://hal.inria.fr/inria-00072957}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72957/filename/RR-3711.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/29/57/PS/RR-3711.ps}, |
| keyword |
= |
{EM algorithm, Particle tracking} |
| } |
Résumé :
Nous considérons l'algorithme PMHT pour suivre la trajectoire de particules dans des détecteurs utilisés en physique des hautes énergies. Cet algorithme a récemment été développé pour suivre des cibles multiples dans un environneme- nt encombré. Il est fondé sur l'estimateur du maximum de vraisemblance, et s'appuie sur un algorithme de type EM. L'algorithme résultant correspond à l'utilisation en parallèle de plusieurs filtres de Kalman itératifs couplés. Il est proche de l'algorithme EA, mais il est de plus capable de prendre en compte le bruit associé au processus, comme par exemple la diffusion de Coulomb multiple. Dans ce rapport, nous présentons les propriétés classiques d'un tel algorithme et proposons une généralisation incluant un recuit déterministe. Nous proposons également plusieurs modificati- ons améliorant les performances de cet algorithme. En particulier, nous avons modifié les probabilités reliant les événements élémentaires aux trajectoires afin d'obtenir une compétition entre ces événements dans une même couche du détecteur. Enfin, nous présentons des résultats obtenus sur des simulations réalisées à partir du détecteur ATLAS (TRT). Nous considérons l'algorithme PMHT pour suivre la trajectoire de particules dans des détecteurs utilisés en physique des hautes énergies. Cet algorithme a récemment été développé pour suivre des cibles multiples dans un environneme- nt encombré. Il est fondé sur l'estimateur du maximum de vraisemblance, et s'appuie sur un algorithme de type EM. L'algorithme résultant correspond à l'utilisation en parallèle de plusieurs filtres de Kalman itératifs couplés. Il est proche de l'algorithme EA, mais il est de plus capable de prendre en compte le bruit associé au processus, comme par exemple la diffusion de Coulomb multiple. Dans ce rapport, nous présentons les propriétés classiques d'un tel algorithme et proposons une généralisation incluant un recuit déterministe. Nous proposons également plusieurs modificati- ons améliorant les performances de cet algorithme. En particulier, nous avons modifié les probabilités reliant les événements élémentaires aux trajectoires afin d'obtenir une compétition entre ces événements dans une même couche du détecteur. Enfin, nous présentons des résultats obtenus sur des simulations réalisées à partir du détecteur ATLAS (TRT). |
Abstract :
We introduce the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementat- ion of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). We introduce the Probabilistic Multi-Hypot- hesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementation of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). |
|
3 - Multiphase Evolution and Image Classification.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. Research Report 3662, INRIA, April 1999.
@TECHREPORT{rr3662,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Multiphase Evolution and Image Classification}, |
| year |
= |
{1999}, |
| month |
= |
{April}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{3662}, |
| url |
= |
{https://hal.inria.fr/inria-00073010}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73010/filename/RR-3662.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/30/10/PS/RR-3662.ps}, |
| keyword |
= |
{} |
| } |
Résumé :
Dans ce rapport, nous présentons un modèle de classification supervisée basé sur une approche variationnelle. Nous souhaitons obtenir une partition optimale de l'image constituée de classes homogènes séparées par des interface- s régulières. Pour cela, nous représentons les régions définies par les classes ainsi que leurs interfaces par des fonctions d'ensembles de niveaux. Nous définissons une fonctionnelle sur ces ensembles de niveaux dont le minimum est une partition optimale. Les Equations aux Dérivées Partielles (EDP) relatives à la minimisation de la fonctionnelle sont couplées et plongées dans une schéma dynamique. En fixant un ensemble de niveaux initial, les différents termes des EDP guident l'évolution des interfaces (ensembles de niveaux zéro) vers les frontières de la partition optimale, par le biais de forces internes (régularité de l'interface) et externes (attache aux données et pas de chevauchement des régions ni de vide dans la partition). Nous avons effectué de nombreux tests sur des images synthétiques ainsi que sur des images réelles. |
Abstract :
This report presents a supervised classification model based on a variational approach. This model is devoted to find an optimal partition compound of homogeneous classes with regular interfaces. We represent the regions of the image defined by the classes and their interfaces by level set functions, and we define a functional whose minimum is an optimal partition. The coupled Partial Differential Equations (PDE) related to the minimization of the functional are considered through a dynamical scheme. Given an initial interface set (zero level set), the different terms of the PDE's are governing the motion of interfaces such that, at convergence, we get an optimal partition as defined above. Each interface is guided by internal forces (regularity of the interface), and external ones (data term, no vacuum, no regions overlapping). We conducted several experiments on both synthetic an real images. |
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