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Publications of Radu Stoica
Result of the query in the list of publications :
2 Articles |
1 - A Gibbs point process for road extraction in remotely sensed images.
R. Stoica and X. Descombes and J. Zerubia. International Journal of Computer Vision, 57(2): pages 121--136, 2004.
@ARTICLE{STO04a,
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| author |
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{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
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{A Gibbs point process for road extraction in remotely sensed images}, |
| year |
= |
{2004}, |
| journal |
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{International Journal of Computer Vision}, |
| volume |
= |
{57}, |
| number |
= |
{2}, |
| pages |
= |
{121--136}, |
| url |
= |
{http://www.springerlink.com/content/kr262t6084464n30/}, |
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{} |
| } |
|
2 - 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 |
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{A RJMCMC algorithm for object processes in image processing}, |
| year |
= |
{2001}, |
| journal |
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{Monte Carlo Methods and Applications}, |
| volume |
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{7}, |
| number |
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{1-2}, |
| pages |
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{149-156}, |
| url |
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{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}, |
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{} |
| } |
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PhD Thesis and Habilitation |
1 - Processus ponctuels pour l'extraction de réseaux linéiques dans les images satellitaires et aériennes.
R. Stoica. PhD Thesis, Universite de Nice Sophia Antipolis, February 2001.
Keywords : Marked point process, Line networks, Road network, Stochastic geometry, RJMCMC.
@PHDTHESIS{rs01,
|
| author |
= |
{Stoica, R.}, |
| title |
= |
{Processus ponctuels pour l'extraction de réseaux linéiques dans les images satellitaires et aériennes}, |
| year |
= |
{2001}, |
| month |
= |
{February}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{Theses/These-stoica.pdf}, |
| keyword |
= |
{Marked point process, Line networks, Road network, Stochastic geometry, RJMCMC} |
| } |
Résumé :
Les réseaux routiers, ou les réseaux hydrographiques, les vaisseaux sanguins ou bien les fissures dans les matériaux sont connus dans la communauté du traitement d'image sous le nom générique de réseaux liné¨iques. La théorie des processus ponctuels marqués est un cadre mathématique rigoureux qui donne la possibilité de modéliser l'image comme un ensemble d'objets en interaction. Les deux idées principales qui ont motivé ce travail sont : ces réseaux sont approchés par de segments de droite connectés, et les réseaux liné¨iques dans une image sont la réalisation d'un processus ponctuel de Gibbs. Le processus ponctuel qui modèlise les réseaux comporte deux composantes. Le premier terme ("Candy" modèle) gère les états et les interactions entre segments : densité, connectivité, alignement et répulsion des segments. L'emplacement du réseau dans l'image est trouvé grâce au second terme, le terme d'attache aux données. Cette composante du modèle est construite à partir de tests d'hypothèses. L'estimateur des réseaux dans l'image est donné par le minimum d'une fonction d'énergie de Gibbs. Pour trouver l'optimum global de cette fonction, nous mettons en {\oe}uvre un algorithme de type recuit simulé qui s'appuie, sur une dynamique de type Monte Carlo par Chaînes de Markov (MCMC) à sauts réversibles. Des résultats sont présentes sur des images aériennes, SPOT et RADAR (RSO). Nous abordons ensuite deux de problèmes ouverts liés au "Candy" modèle, mais d'un interêt théorique général : la convergence d'une dynamique de Monte Carlo à sauts reversibles, et l'estimation des paramètres des processus ponctuels. Une solution à ces problèmes pourrait ouvrir une nouvelle direction dans la recherche de méthodes non-supervisése en traitement d'image. |
Abstract :
Road or hydrographical networks, blood vessels or fissures in materials are all known by the image processing community under the general name of line networks. The theory of point processes is a rigourous mathematical framework which allows us to model an image as a set of interacting objects. The two main ideas which are the basis of this work are : these networks can be considered as connected segments, and the line networks in an image are the realization of a Gibbs point process. The point process used to model the networks has two components. The first one (Candy model) deals with the states and the interaction of the segments : density, connectivity, alignment, attraction and rejection. The location of the network is determined by the second component, the data term. This component is based on hypothesis tests. The network estimator is given by the minimum of a Gibbs energy. We build a simulated annealing algorithm in order to avoid local minima. This algorithm uses reversible jump Monte Carlo Markov Chain (RJMCMC) dynamics. Results are shown on aerial, SPOT and RADAR (SAR) images. Finally, we start a study on two open problems related to the Candy model, but of general theoretical interest : the convergence of a RJMCMC dynamics, and parameter estimation related to point processes. A solution to these problems would give a new direction for the research of unsupervised methods in image processing. |
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5 Conference articles |
1 - Parameter estimation by a Markov Chain Monte Carlo technique for the Candy-model.
X. Descombes and M.N.M. van Lieshout and R. Stoica and J. Zerubia. In IEEE Workshop on Statistical Signal Processing, papier invité, Singapour, August 2001.
@INPROCEEDINGS{xd01e,
|
| author |
= |
{Descombes, X. and van Lieshout, M.N.M. and Stoica, R. and Zerubia, J.}, |
| title |
= |
{Parameter estimation by a Markov Chain Monte Carlo technique for the Candy-model}, |
| year |
= |
{2001}, |
| month |
= |
{August}, |
| booktitle |
= |
{IEEE Workshop on Statistical Signal Processing}, |
| address |
= |
{papier invité, Singapour}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=955212}, |
| keyword |
= |
{} |
| } |
|
2 - Road extraction in remote sensed images using a stochastic geometry framework.
R. Stoica and X. Descombes and J. Zerubia. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Gif sur Yvette, France, July 2000.
@INPROCEEDINGS{xd00b,
|
| author |
= |
{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Road extraction in remote sensed images using a stochastic geometry framework}, |
| year |
= |
{2000}, |
| month |
= |
{July}, |
| booktitle |
= |
{Bayesian Inference and Maximum Entropy Methods in Science and Engineering}, |
| address |
= |
{Gif sur Yvette, France}, |
| url |
= |
{http://www-prod-gif.supelec.fr/invi/lss/MaxEnt2000/}, |
| pdf |
= |
{http://www-prod-gif.supelec.fr/invi/lss/MaxEnt2000/htm/Abstracts/stoica.pdf}, |
| keyword |
= |
{} |
| } |
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3 - 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 |
= |
{} |
| } |
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4 - Image Retrieval and Indexing: A Hierarchical Approach in Computing the Distance between Textured Images.
R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Image Processing (ICIP), Chicago, USA, October 1998.
@INPROCEEDINGS{stoica98a,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{Image Retrieval and Indexing: A Hierarchical Approach in Computing the Distance between Textured Images}, |
| year |
= |
{1998}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=723675}, |
| keyword |
= |
{} |
| } |
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5 - The two-dimensional Wold decomposition for segmentation and indexing in image libraries.
R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, May 1998.
@INPROCEEDINGS{stoica98b,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{The two-dimensional Wold decomposition for segmentation and indexing in image libraries}, |
| year |
= |
{1998}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Seattle, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=678151}, |
| keyword |
= |
{} |
| } |
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2 Technical and Research Reports |
1 - A Markov point process for road extraction in remote sensed images.
R. Stoica and X. Descombes and J. Zerubia. Research Report 3923, Inria, 2000.
Keywords : Stochastic geometry, Marked point process, Candy model, Road network, RJMCMC.
@TECHREPORT{rs00,
|
| author |
= |
{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Markov point process for road extraction in remote sensed images}, |
| year |
= |
{2000}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3923}, |
| url |
= |
{https://hal.inria.fr/inria-00072729}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72729/filename/RR-3923.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/27/29/PS/RR-3923.ps}, |
| keyword |
= |
{Stochastic geometry, Marked point process, Candy model, Road network, RJMCMC} |
| } |
Résumé :
Nous proposons une nouvelle méthode pour extraire les routes dans les images satellitales et aériennes. Notre approche est basée sur la géométrie stochastique et les dynamiques MCMC à saut réversible. Nous considérons que le réseau routier est un réseau fin, et que ce réseau peut être approximé par des segments connectés. Nous construisons un processus ponctuel marqué qui peut simuler et détecter des réseaux fins. La densité de probabilité de ce processus comporte deux termes : le terme d'attache aux données et le terme a priori. Pour former un réseau, les segments doivent être connectés. Nous souhaitons que les segments soient bien alignés et qu'ils ne se superposent pas. Toutes ces contraintes sont prises en compte par le modèle a priori (Candy modèle). L'emplacement du réseau est donné par le terme d'attache aux données. Ce terme est construit à partir des tests d'hypothèses. Notre modèle probabiliste permet de construire le MAP de l'estimateur du réseau linéique. Pour éviter les minima locaux, nous utilisons un algorithme de type recuit simulé, construit sur une dynamique MCMC à sauts réversibles. Nous montrons des résultats sur des images SPOT, ERS et aériennes. |
Abstract :
In this paper we propose a new method to extract roads in remote sensed images. Our approach is based on stochastic geometry theory and reversible jump Monte Carlo Markov Chains dynamic. We consider that roads consist of a thin network in the image. We make the hypothesis that such a network can be approximated by a network composed of connected line segments. We build a marked point process, which is able to simulate and detect thin networks. The segments have to be connected, in order to form a line-netw- ork. Aligned segments are favored whereas superposition is penalized. Those constraints are taken in account by the prior model (Candy model), which is an area-interaction point process.The location of the network and the specifities of a road network in the image are given by the likelihood term. This term is based on statistical hypothesis tests. The proposed probabilistic model yelds a MAP estimator of the road network. In order to avoid local minima, a simulated annealing algorithm, using a reversible jump MCMC dynamic is designed. Results are shown on SPOT, ERS and aerial images. |
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2 - Indexing and retrieval in multimedia libraries through parametric texture modeling using the 2D Wold decomposition.
R. Stoica and J. Zerubia and J.M. Francos. Research Report 3594, Inria, December 1998.
Keywords : Markov Fields, Texture, Segmentation, Indexation.
@TECHREPORT{stoica98,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{Indexing and retrieval in multimedia libraries through parametric texture modeling using the 2D Wold decomposition}, |
| year |
= |
{1998}, |
| month |
= |
{December}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3594}, |
| url |
= |
{https://hal.inria.fr/inria-00073085}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73085/filename/RR-3594.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/30/85/PS/RR-3594.ps}, |
| keyword |
= |
{Markov Fields, Texture, Segmentation, Indexation} |
| } |
Résumé :
Ce rapport présente une méthode paramétrique permettant de faire de l'indexati- on et de la recherche dans une base de données multimédia. L'indexation (étiquetage) et la recherche de données multimédia sont réalisées grâce à la modélisation paramétrique de textures qui se trouvent dans les images de la base de données. Les textures sont caracterisées par des paramètres qui servent d'indices pour la recherche dans la base de données. Afin de pouvoir identifier les différentes régions texturées d'une image et estimer les paramètres correspondants, un algorithme de segmentation-estimatio- n est proposé dans ce rapport, qui fait appel à une décomposition de Wold 2D pour le modèle de texture et à un modèle markovien pour l'étiquetage. L'indexation nécessite de définir une distance entre les images. Une nouvelle distance, inspirée de la distance de Kullback, est décrite dans ce rapport. Elle utilise les paramètres estimés correspondants au modèle 2D de chaque texture. Les résultats obtenus relativement à la segmentation et à l'indexatio- n sont proches de ceux obtenus par un opérateur humain. |
Abstract :
This paper presents a parametric method for indexing and retrieval of multimedia data in digital libraries. %Indexing (labeling) and retrieval %of multimedia data, based on the properties %of the imagery components of the stored data record, are derived. Indexing (labeling) and retrieval of the multimedia data are performed using parametric modeling of the textured segments found in the data imagery components. The estimated parametric models of the textured segments serve as their indices, and hence as indices of the entire image, as well as of the multimedia record which the image is part thereof. To achieve the ability to identify textured image regions and estimate their parameters, a joint segmentation-estimation algorithm that combines the 2-D Wold decomposition based texture model with a Markovian labeling process, is derived. Ordering and indexing of images require a definition of a distance measure between images. Using the framework of the Kullback distance between probability distributions, a new rigorous distance measure between textures is derived. The distance between any two textured image segments is evaluated using their estimated parametric models. The proposed segmentation, distance evaluation, and indexing methods are shown to produce comparable results to those obtained by a human viewer. |
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Collection article or Book chapter |
1 - An application of marked point process to the extraction of linear networks for images.
R. Stoica and X. Descombes and M.N.M. Van Lieshout and J. Zerubia. In Spatial statitics through applications, Publ. WITPress, 2002.
Keywords : Line networks, Road network, Object extraction, Satellite images, Marked point process.
@INCOLLECTION{stoicaXDlivre,
|
| author |
= |
{Stoica, R. and Descombes, X. and Van Lieshout, M.N.M. and Zerubia, J.}, |
| title |
= |
{An application of marked point process to the extraction of linear networks for images}, |
| year |
= |
{2002}, |
| booktitle |
= |
{Spatial statitics through applications}, |
| publisher |
= |
{WITPress}, |
| url |
= |
{http://www.witpress.com/books/978-1-85312-649-9}, |
| pdf |
= |
{http://oai.cwi.nl/oai/asset/10645/10645A.pdf}, |
| keyword |
= |
{Line networks, Road network, Object extraction, Satellite images, Marked point process} |
| } |
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