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Publications about Marked point process
Result of the query in the list of publications :
15 Conference articles |
11 - Galaxy filament detection using the Quality candy model.
P. Gernez and X. Descombes and J. Zerubia and E. Slezak and A. Bijaoui. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006.
Keywords : Marked point process, Quality Candy model, Galaxy Filaments.
@INPROCEEDINGS{gernez06,
|
| author |
= |
{Gernez, P. and Descombes, X. and Zerubia, J. and Slezak, E. and Bijaoui, A.}, |
| title |
= |
{Galaxy filament detection using the Quality candy model}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_gernez06.pdf}, |
| keyword |
= |
{Marked point process, Quality Candy model, Galaxy Filaments} |
| } |
|
12 - Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), St Augustine, Florida, USA, November 2005.
Keywords : Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC.
Copyright : Springer Verlag
@INPROCEEDINGS{perrin_emmcvpr05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{St Augustine, Florida, USA}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.ps.gz}, |
| keyword |
= |
{Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC} |
| } |
Abstract :
We use marked point processes to detect an unknown number of trees from high resolution aerial images. This is in fact an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects to the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects.
We compare here different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels. |
|
13 - A Marked Point Process Model for Tree Crown Extraction in Plantations.
G. Perrin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process.
@INPROCEEDINGS{perrin_icip05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process Model for Tree Crown Extraction in Plantations}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.ps.gz}, |
| keyword |
= |
{Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process} |
| } |
Abstract :
This work presents a framework to extract tree crowns from remotely sensed data, especially in plantation images, using stochastic geometry. We aim at finding the tree top positions, and the tree crown diameter distribution. Our approach consists in considering that these images are some realizations of a marked point process. First we model the tree plantation as a configuration of an unknown number of ellipses. Then, a Bayesian energy is defined, containing both a prior energy which incorporates the prior knowledge of the plantation geometric properties, and a likelihood which fits the objects to the data. Eventually, we estimate the global minimum of this energy using Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing scheme. We present results on optical aerial images of poplars provided by IFN. |
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14 - Tree Crown Extraction using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
Keywords : RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry.
@INPROCEEDINGS{perrin04a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Tree Crown Extraction using Marked Point Processes}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.ps.gz}, |
| keyword |
= |
{RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry} |
| } |
Abstract :
In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process.
Then, we use a Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels.
Results are shown on aerial images of poplars provided by IFN. |
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15 - Marked Point Process in Image Analysis : from Context to Geometry.
X. Descombes and F. Kruggel and C. Lacoste and M. Ortner and G. Perrin and J. Zerubia. In International Conference on Spatial Point Process Modelling and its Application (SPPA), Castellon, Spain, 2004.
Keywords : RJMCMC, Object extraction, Marked point process, Stochastic geometry.
@INPROCEEDINGS{geostoch04a,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Lacoste, C. and Ortner, M. and Perrin, G. and Zerubia, J.}, |
| title |
= |
{Marked Point Process in Image Analysis : from Context to Geometry}, |
| year |
= |
{2004}, |
| booktitle |
= |
{International Conference on Spatial Point Process Modelling and its Application (SPPA)}, |
| address |
= |
{Castellon, Spain}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.ps.gz}, |
| keyword |
= |
{RJMCMC, Object extraction, Marked point process, Stochastic geometry} |
| } |
Abstract :
We consider the marked point process framework as a natural extension of the Markov random field approach in image analysis. We consider a general model defined by its density allowing us to consider some geometrical constraints on objects and between objects in feature extraction problems. Some examples are derived for small brain lesions detection from MR Images, road network, tree crown and building extraction from remotely sensed images. The results obtained on real data show the relevance of the proposal approach. |
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15 Technical and Research Reports |
1 - Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution .
S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. Rapport de recherche 7350, INRIA, July 2010.
Keywords : Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction.
@TECHREPORT{RR-7350,
|
| author |
= |
{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution }, |
| year |
= |
{2010}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7350}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00508431/fr/}, |
| keyword |
= |
{Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction} |
| } |
|
2 - Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach.
C. Benedek and X. Descombes and J. Zerubia. Research Report 7143, INRIA, Sophia Antipolis, December 2009.
Keywords : Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics.
@TECHREPORT{benedekRR_09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7143}, |
| address |
= |
{Sophia Antipolis}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00426615}, |
| keyword |
= |
{Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle méthode probabiliste qui intègre l'extraction de bâtiments et la détection de changements à partir de paires d'images de télédétection. Un algorithme d'optimisation globale permet de trouver la configuration optimale de bâtiments en considérant des observations, des connaissances a priori et des interactions entre des parties voisines de bâtiments. La précision est assurée par une vérification d'un modèle objet bayésien; le coût du calcul est considérablement réduit en utilisant un processus stochastique non-uniforme de naissance d'objets fondé sur des caractéristiques bas-niveaux des images, qui génère des objets pertinents ayant une grande probabilité. |
Abstract :
In this report we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features. |
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3 - Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations.
S. Descamps and X. Descombes and A. Béchet and J. Zerubia. Research Report 6328, INRIA, October 2007.
Keywords : Object extraction, modélisation stochastique , Marked point process, dynamique de naissance/mort, environnement, flamants roses.
@TECHREPORT{Descamps-Descombes,
|
| author |
= |
{Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, |
| title |
= |
{Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations}, |
| year |
= |
{2007}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6328}, |
| url |
= |
{http://hal.inria.fr/inria-00180811}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/18/08/93/PDF/RR-Desc-Desc-Bech-Zeru.pdf}, |
| keyword |
= |
{Object extraction, modélisation stochastique , Marked point process, dynamique de naissance/mort, environnement, flamants roses} |
| } |
|
4 - An automatic building extraction method : Application to the 3D-city modeling.
F. Lafarge and P. Trontin and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5925, INRIA, France, May 2006.
Keywords : Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM).
@TECHREPORT{lafarge_rr_may06,
|
| author |
= |
{Lafarge, F. and Trontin, P. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An automatic building extraction method : Application to the 3D-city modeling}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5925}, |
| address |
= |
{France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_rr_may06.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM)} |
| } |
|
5 - A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5846, INRIA, France, February 2006.
Keywords : Data energy, Object extraction, Tree Crown Extraction, Marked point process, Stochastic geometry, 3D reconstruction.
@TECHREPORT{rr_perrin_nonbay_05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes}, |
| year |
= |
{2006}, |
| month |
= |
{February}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5846}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070180/fr/}, |
| pdf |
= |
{http://hal.inria.fr/inria-00070180/fr/}, |
| keyword |
= |
{Data energy, Object extraction, Tree Crown Extraction, Marked point process, Stochastic geometry, 3D reconstruction} |
| } |
Résumé :
Dans ce rapport de recherche, notre but est d'extraire les houppiers à partir d'images aériennes de forêts à l'aide de processus ponctuels marqués d'ellipses ou d'ellipsoïdes. Notre approche consiste, en effet, à modéliser les données comme des réalisations de tels processus. Une fois l'objet géométrique de référence choisi, nous échantillonnons le processus objet défini par une densité grâce à un algorithme MCMC à sauts réversibles, optimisé par un recuit simulé afin d'extraire la meilleure configuration d'objets, qui nous donne l'extraction recherchée.
Nous obtenons ainsi le nombre des arbres, leur localisation et leur taille. Nous présentons, dans ce rapport, un modèle 2D et un modèle 3D pour extraire des statistiques forestières. Ceux-ci sont testés sur des images aériennes infrarouge couleur très haute résolution fournies par l'Inventaire Forestier National (IFN). |
Abstract :
High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable forestry managers to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the resources. Automatic algorithms are needed in that prospect to assist human operators in the exploitation of these data. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the locations of the trees and marks their geometric features. As a result we obtain the number of stems, their position, and their size. This approach yields an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted, in 2D and 3D. Results are shown on Colour Infrared aerial images provided by the French National Forest Inventory (IFN) |
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