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Publications about RJMCMC
Result of the query in the list of publications :
2 Articles |
1 - Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires.
S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. Revue Française de Photogrammétrie et de Télédétection (SFPT), (194): pages 2-15, 2011.
Keywords : processus ponctuel marqué, RJMCMC, Simulated Annealing, SEM, pseudo-vraisemblance, extraction d'objet..
@ARTICLE{RFPT_SBH_11,
|
| author |
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{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
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{Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires}, |
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| } |
|
2 - Building Outline Extraction from Digital Elevation Models using Marked Point Processes.
M. Ortner and X. Descombes and J. Zerubia. International Journal of Computer Vision, 72(2): pages 107-132, April 2007.
Keywords : RJMCMC, Buildings, Stochastic geometry, Marked point process, Digital Elevation Model (DEM).
@ARTICLE{ortner_ijcv_05,
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{Building Outline Extraction from Digital Elevation Models using Marked Point Processes}, |
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{2007}, |
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{107-132}, |
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| keyword |
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{RJMCMC, Buildings, Stochastic geometry, Marked point process, Digital Elevation Model (DEM)} |
| } |
|
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4 PhD Thesis and Habilitations |
1 - Etude du couvert forestier par processus ponctuels marqués.
G. Perrin. PhD Thesis, Ecole Centrale Paris, October 2006.
Keywords : Tree Crown Extraction, Marked point process, Stochastic geometry, Object extraction, RJMCMC.
@PHDTHESIS{perrin_phd06,
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{2006}, |
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{Tree Crown Extraction, Marked point process, Stochastic geometry, Object extraction, RJMCMC} |
| } |
Résumé :
Cette thèse aborde le problème de l'extraction d'arbres à partir d'images aériennes InfraRouge Couleur (IRC) de forêts. Nos modèles reposent sur l'utilisation de processus objets ou processus ponctuels marqués. Il s'agit de variables aléatoires dont les réalisations sont des configurations d'objets géométriques. Une fois l'objet géométrique de référence choisi, nous définissons l'énergie du processus par le biais d'un terme a priori, modélisant les contraintes sur les objets et leurs interactions, ainsi qu'un terme image. Nous échantillonnons le processus objet grâce à un algorithme de type Monte Carlo par Chaînes de Markov à sauts réversibles (RJMCMC), optimisé par un recuit simulé afin d'extraire la meilleure configuration d'objets, qui nous donne l'extraction recherchée.
Dans ce manuscrit, nous proposons différents modèles d'extraction de houppiers, qui extraient des informations à l'échelle de l'arbre selon la densité du peuplement. Dans les peuplements denses, nous présentons un processus d'ellipses, et dans les zones de plus faible densité, un processus d'ellipsoïdes. Nous obtenons ainsi le nombre d'arbres, leur localisation, le diamètre de la couronne et leur hauteur pour les zones non denses. Les algorithmes automatiques résultant de cette modélisation sont testés sur des images IRC très haute résolution fournies par l'Inventaire Forestier National (IFN). |
Abstract :
This thesis addresses the problem of tree crown extraction from Colour InfraRed (CIR) aerial images of forests. Our models are based on object processes, otherwise known as marked point processes. These mathematical objects are random variables whose realizations are configurations of geometrical shapes. This approach yields an energy minimization problem, where the energy is composed of a regularization term (prior density), which introduces some constraints on the objects and their interactions, and a data term, which links the objects to the features to be extracted. Once the reference object has been chosen, we sample the process and extract the best configuration of objects with respect to the energy, using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm embedded in a Simulated Annealing scheme.
We propose different models for tree crown extraction depending on the density of the stand. In dense areas, we use an ellipse process, while in sparse vegetation an ellipsoïd process is used. As a result we obtain the number of stems, their position, the diameters of the crowns and the heights of the trees for sparse areas. The resulting algorithms are tested on high resolution CIR aerial images provided by the French National Forest Inventory (IFN). |
|
2 - Processus Ponctuels Marqués pour l'Extraction Automatique de Caricatures de Bâtiments à partir de Modèles Numériques d'Elévation.
M. Ortner. PhD Thesis, Universite de Nice Sophia Antipolis, October 2004.
Keywords : Marked point process, Object extraction, Buildings, Digital Elevation Model (DEM), RJMCMC, Stochastic geometry.
@PHDTHESIS{mortner_these,
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{Ortner, M.}, |
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{Processus Ponctuels Marqués pour l'Extraction Automatique de Caricatures de Bâtiments à partir de Modèles Numériques d'Elévation}, |
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{Marked point process, Object extraction, Buildings, Digital Elevation Model (DEM), RJMCMC, Stochastic geometry} |
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Résumé :
Cette thèse se place dans un cadre de reconstruction urbaine et propose un corpus algorithmique pour extraire des formes simples sur les Modèles Numériques d'Elévation. Ce type de données décrit le relief d'une zone urbaine par une grille régulière de points à chacun desquels est associée une information de hauteur.
Les modèles utilisés reposent sur l'utilisation de processus ponctuels marqués. Il s'agit de variables aléatoires dont les réalisations sont des configurations d'objets géométriques. Ces modèles permettent d'introduire des contraintes sur la forme des objets recherchés dans une image ainsi qu'un terme de régularisation modélisé par des interactions entre les objets. Une énergie peut être associée aux configurations d'objets et la configuration minimisant cette énergie trouvée au moyen d'un recuit-simulé couplé à un échantillonneur de type Monte Carlo par Chaîne de Markov à sauts réversibles (RJMCMC).
Nous proposons quatre modèles pour extraire des caricatures de bâtiments à partir de descriptions altimétriques de zones urbaines denses. Chaque modèle est constitué par une forme d'objet, une énergie d'attache aux données et une énergie de régularisation. Les deux premiers modèles permettent d'extraire des formes simples (rectangles) en utilisant une contrainte d'homogénéité pour l'un et une détection des discontinuités pour l'autre. Le troisième modèle modélise les bâtiments par une forme polyhédrique. Le dernier modèle s'intéresse à l'apport d'une coopération entre des objets simples. Les algorithmes obtenus, automatiques, sont évalués sur des données réelles fournies par l'IGN (MNE Laser et optiques de différentes qualités). |
Abstract :
The context of this thesis is the reconstruction of urban areas from images. It proposes a set of algorithms for extracting simple shapes from Digital Elevation Models (DEM). DEMs describe the altimetry of an urban area by a grid of points, each of which has a height associated to it.
The proposed models are based on marked point processes. These mathematical objects are random variables whose realizations are configurations of geometrical shapes. Using these processes, we can introduce constraints on the shape of the objects to be detected in an image, and a regularizing term incorporating geometrical interactions between objects. An energy can be associated to each object configuration, and the global minima of this energy can then be found by applying simulated annealing to a Reversible Jump Monte Carlo Markov Chain sampler (RJMCMC).
We propose four different models for extracting the outlines of buildings from altimetric descriptions of dense urban areas. Each of these models is constructed from an object shape, a data energy, and a regularizing energy.
The first two models extract simple shapes (rectangles) using, respectively, a homogeneity constraint and discontinuity detection. The third model looks for three-dimensional polyhedral buildings. The last model uses cooperation between two types of objects, rectangles and segments.
The resulting algorithms are evaluated on real data provided by the French National Geographic Institute (a laser DEM and optical DEMs of differing quality). |
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3 - Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués.
C. Lacoste. PhD Thesis, Universite de Nice Sophia Antipolis, September 2004.
Keywords : Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process.
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{Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués}, |
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Résumé :
Cette thèse aborde le problème de l'extraction non supervisée des réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires et aériennes. Nous utilisons des processus objet, ou processus ponctuels marqués, comme modèles a priori. Ces modèles permettent de bénéficier de l'apport d'un cadre stochastique (robustesse au bruit, corpus algorithmique, etc.) tout en manipulant des contraintes géométriques fortes. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation.
Nous proposons tout d'abord une modélisation du réseau linéique par un processus dont les objets sont des segments interagissant entre eux. Le modèle a priori est construit de façon à exploiter au mieux la topologie du réseau recherché au travers de potentiels fondés sur la qualité de chaque interaction. Les propriétés radiométriques sont prises en compte dans un terme d'attache aux données fondé sur des mesures statistiques.
Nous étendons ensuite cette modélisation à des objets plus complexes. La manipulation de lignes brisées permet une extraction plus précise du réseau et améliore la détection des bifurcations.
Enfin, nous proposons une modélisation hiérarchique des réseaux hydrographiques dans laquelle les affluents d'un fleuve sont modélisés par un processus de lignes brisées dans le voisinage de ce fleuve.
Pour chacun des modèles, nous accélérons la convergence de l'algorithme MCMC par l'ajout de perturbations adaptées.
La pertinence de cette modélisation par processus objet est vérifiée sur des images satellitaires et aériennes, optiques et radar. |
Abstract :
This thesis addresses the problem of the unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. We use object processes, or marked point processes, as prior models. These models benefit from a stochastic framework (robustness w.r.t. noise, algorithms, etc.) while incorporating strong geometric constraints. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization.
We first propose to model line networks by a process whose objects are interacting line segments. The prior model is designed to exploit as fully as possible the topological properties of the network under consideration through potentials based on the quality of each interaction. The radiometric properties of the network are modeled using a data term based on statistical measures.
We then extend this model to more complex objects. The use of broken lines improves the detection of network junctions and increases the accuracy of the extracted network.
Finally, we propose a hierarchical model of hydrographic networks in which the tributaries of a given river are modeled by a process of broken lines in the neighborhood of this river. For each model, we accelerate convergence of the RJMCMC algorithm by using appropriate perturbations.
We show experimental results on aerial and satellite images (optical and radar data) to verify the relevance of the object process models. |
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4 - 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 |
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{Processus ponctuels pour l'extraction de réseaux linéiques dans les images satellitaires et aériennes}, |
| year |
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{2001}, |
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{February}, |
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{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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8 Conference articles |
1 - Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images.
S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010.
Keywords : Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM).
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|
2 - Lidar Waveform Modeling using a Marked Point Process.
C. Mallet and F. Lafarge and F. Bretar and U. Soergel and C. Heipke. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009.
Keywords : 3D point cloud, Lidar, Marked point process, RJMCMC.
@INPROCEEDINGS{mallet_icip09,
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{Mallet, C. and Lafarge, F. and Bretar, F. and Soergel, U. and Heipke, C.}, |
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{Lidar Waveform Modeling using a Marked Point Process}, |
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{2009}, |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
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{http://dx.doi.org/10.1109/ICIP.2009.5413380}, |
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| } |
Abstract :
Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas. |
|
3 - An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), Atlanta, October 2006.
Keywords : 3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images.
Copyright : IEEE
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{An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images}, |
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|
4 - Forest Resource Assessment using Stochastic Geometry.
G. Perrin and X. Descombes and J. Zerubia and J.G. Boureau. In Proc. International Precision Forestry Symposium, March 2006.
Keywords : Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy.
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{Forest Resource Assessment using Stochastic Geometry}, |
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Abstract :
Aerial and satellite imagery has a key role to play in natural resource management, especially in forestry application. The submetric resolution of the data enables to study forests at the scale of trees, and to get a more accurate assessment of the resources such as the number of stems or the forest cover. To develop automatic tools in order to help the inventories in their work and to bring more knowledge about the stands is also nowadays of important economical and environmental concerns.
In this paper, we aim at extracting tree crowns from high resolution aerial Color Infrared images (CIR) of forests using marked point processes. Our approach consists in modelling the trees in the forestry images as random configurations of ellipses, whose points are the positions of the stems and marks their geometric features. The density of this process 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. Our goal is to find the best configuration of an unknown number of objects, i.e. the configuration that maximizes this density. To sample this marked point process, we use Monte Carlo dynamics while the optimization is performed via a Simulated Annealing algorithm, which results in a fully automatic approach.
We present different models for the data term in order to cope with different kinds of stands : plantations, isolated trees and mixed stands. Results are shown on aerial CIR images provided by the French Forest Inventory (IFN) |
|
5 - 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
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{Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, |
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{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
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| } |
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. |
|
6 - 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,
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{A Marked Point Process Model for Tree Crown Extraction in Plantations}, |
| year |
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{2005}, |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
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| } |
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. |
|
7 - 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. |
|
8 - 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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12 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 - A structural approach for 3D building reconstruction.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 6048, INRIA, November 2006.
Keywords : 3D reconstruction, Structural approach, Building, RJMCMC, Viterbi.
@TECHREPORT{Lafarge_rr_6048,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A structural approach for 3D building reconstruction}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6048}, |
| url |
= |
{https://hal.inria.fr/inria-00114338}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Lafarge_rr_6048.pdf}, |
| keyword |
= |
{3D reconstruction, Structural approach, Building, RJMCMC, Viterbi} |
| } |
|
3 - A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models..
M. Ortner and X. Descombes and J. Zerubia. Research Report 5712, INRIA, France, October 2005.
Keywords : Marked point process, Buildings, RJMCMC.
@TECHREPORT{ortner-RR05,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models.}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5712}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070305}, |
| keyword |
= |
{Marked point process, Buildings, RJMCMC} |
| } |
Résumé :
Ce travail présente une approche par géométrie stochastique pour l'extraction de primitives dans les images. Ces structures sont modélisées sous forme de réalisations d'un processus ponctuel spatial marqué dont les points sont des formes géométriques. Cette approche permet d'incorporer un modèle a priori sur la répartition spatiale des structures d'intérêt. Plus spécifiquement, nous présentons un modèle fondé sur l'interaction d'un processus de rectangles avec un processus de segments. Le premier est dédié à la détection des zones homogènes dans l'image et le second à la détection des discontinuités significatives. Nous définissons l'énergie d'une configuration de façon à favoriser la connection entre les segments, l'alignement des rectangles et l'adéquation entre les deux types de primitives. L'estimation repose sur l'emploi d'une technique de recuit-simulé. Le modèle proposé est appliqué à l'analyse de Modèles Numériques d'Elevation. Nous présentons des résultats sur des données réelles fournies par l'Institut Géographique National (IGN). Nous montrons en particulier que l'approche est efficace sur des données de types très différents. |
Abstract :
A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.
This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a prior knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types. |
|
4 - A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5687, INRIA, France, September 2005.
Keywords : 3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM).
@TECHREPORT{5687,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5687}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070326/fr/}, |
| pdf |
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| ps |
= |
{http://hal.inria.fr/docs/00/07/03/26/PS/RR-5687.ps}, |
| keyword |
= |
{3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM)} |
| } |
Résumé :
Dans ce rapport, nous développons un modèle paramétrique pour la reconstruction automatique de bâtiments en 3D fondé sur une approche bayésienne à partir de simulations PLEIADES. Les images satellitaires haute résolution représentent un nouveau type de données permettant de traiter les problèmes de reconstruction 3D de bâtiments. Leur résolution ``relativement basse'' et leur faible rapport signal sur bruit pour ce type de problèmes ne permet pas l'utilisation des méthodes standard développées dans le cas des images aériennes. Nous proposons une approche paramétrique utilisant des Modèles Numériques d'Elévation (MNE) et les empreintes de bâtiments associées modélisées par rectangles. La méthode proposée est fondée sur une approche bayésienne. Une technique de type de Monte Carlo par Chaînes de Markov est utilisée afin d'optimiser le modèle énergétique. |
Abstract :
This report develops a parametric model for automatic 3D building reconstruction based on a Bayesian approach from PLEIADES simulations. High resolution satellite images are a new kind of data to deal with 3D building reconstruction problems. Their ``relatively low'' resolution and low signal noise ration do not allow to use standard methods developed for the aerial image case. We propose a parametric approach using Digital Elevation Models (DEM) and associated rectangular building footprints. The proposed method is based on a Bayesian approach. A Markov Chain Monte Carlo technique is used to optimize the energy model. |
|
5 - Point Processes in Forestry : an Application to Tree Crown Detection.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5544, INRIA, France, April 2005.
Keywords : Marked point process, Object extraction, RJMCMC, Tree Crown Extraction, Stochastic geometry.
@TECHREPORT{5544,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point Processes in Forestry : an Application to Tree Crown Detection}, |
| year |
= |
{2005}, |
| month |
= |
{April}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
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{5544}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070463}, |
| pdf |
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{https://hal.inria.fr/file/index/docid/70463/filename/RR-5544.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/04/63/PS/RR-5544.ps}, |
| keyword |
= |
{Marked point process, Object extraction, RJMCMC, Tree Crown Extraction, Stochastic geometry} |
| } |
Résumé :
Dans ce rapport de recherche, notre but est d'extraire des houppiers à partir d'images aériennes de forêts à l'aide de processus ponctuels marqués de disques et d'ellipses. 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 le maximum a posteriori de cette densité. Cette configuration optimale nous donnera l'extraction recherchée.
Dans une première partie, nous proposons de revenir quelque peu sur les processus ponctuels marqués et leur application dans la foresterie. Puis, nous présentons deux nouveaux modèles d'extraction de houppiers à base de disques et d'ellipses, et discutons de quelques améliorations au niveau de la simulation et de l'optimisation de notre algorithme.
Nous présentons des résultats obtenus sur des images aériennes très haute résolution fournies par l'Inventaire Forestier National (IFN), ainsi que sur des images synthétiques simulées avec le logiciel AMAP (Bionatics, projet Digiplante). |
Abstract :
In this research report, we aim at extracting tree crowns from remotely sensed images using marked point processes of discs and ellipses. Our approach is indeed to consider that the data are some realizations of a marked point process. Once a geometrical object is defined, we sample a marked point process defined by a density with a Reversible Jump Markov Chain Monte Carlo dynamics and simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image.
In a first part, we propose to review the basis of marked point processes and some of their examples used in forestry statistic inference. Then, we present two new models, with discs and ellipses, and discuss some improvements made in the optimization or in the simulation.
Results are shown on high resolution aerial images of poplars provided by the French Forest Inventory (IFN), and synthetic images simulated with AMAP software (Bionatics, Digiplante project). |
|
6 - Extraction de Houppiers par Processus Objet.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5037, INRIA, France, December 2003.
Keywords : Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process, RJMCMC.
@TECHREPORT{Perrin03,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction de Houppiers par Processus Objet}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5037}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071547}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71547/filename/RR-5037.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/47/PS/RR-5037.ps}, |
| keyword |
= |
{Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process, RJMCMC} |
| } |
Résumé :
Nous cherchons à extraire des houppiers à partir d'images de télédétection. Pour ce faire, nous construisons un processus objet et assimilons nos images d'arbres à des réalisations de ce processus. La première étape consiste à définir d'une part les objets géométriques modélisant les arbres, et d'autre part la densité du processus à simuler.La seconde étape consiste à construire un algorithme MCMC à sauts réversibles, et une estimée de la configuration d'objets. Les transitions aléatoires de la chaîne sont régies par des noyaux de propositions, chacun étant associé à une perturbation.Nous testons notre modèle sur des images aériennes de peupleraies fournies par l'IFN. |
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 MCMC dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crowns distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels.Results are shown on aerial images of poplars given by IFN. |
|
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