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Publications about Marked point process
Result of the query in the list of publications :
15 Conference articles |
1 - Multiple Birth and Cut Algorithm for Point Process Optimization.
A. Gamal Eldin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS), Kuala Lumpur, Malaysia, December 2010.
Keywords : Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process.
@INPROCEEDINGS{MBC_MPP_SITIS10,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Multiple Birth and Cut Algorithm for Point Process Optimization}, |
| year |
= |
{2010}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS)}, |
| address |
= |
{Kuala Lumpur, Malaysia}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00516305/fr/}, |
| keyword |
= |
{Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process} |
| } |
Abstract :
In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to the energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing the advantages and disadvantages, where the most important advantage is the reduction of the number of parameters. We validated our algorithm on the counting problem of flamingos in colony, where our algorithm outperforms the performance of the MBD algorithm. |
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2 - 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).
@INPROCEEDINGS{sbenhadj10a,
|
| author |
= |
{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://hal.archives-ouvertes.fr/docs/00/52/63/45/PDF/ISPRS_SBH_FC_XD_JZ_Final2.pdf}, |
| keyword |
= |
{Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM)} |
| } |
|
3 - Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles.
C. Benedek and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, August 2010.
Keywords : Marked point process, multiple birth-and-death dynamics, Building extraction.
@INPROCEEDINGS{benedekICPR10,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles}, |
| year |
= |
{2010}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Istanbul, Turkey}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00481019/en/}, |
| keyword |
= |
{Marked point process, multiple birth-and-death dynamics, Building extraction} |
| } |
Abstract :
In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques. |
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4 - Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours.
M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IS&T/SPIE Electronic Imaging, San Jose, USA, January 2010.
Keywords : Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics.
Copyright : Copyright 2010 by SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE Electronic Imaging 2010 Conference in San Jose, USA, and is made available as an electronic reprint with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
@INPROCEEDINGS{Kulikova10a,
|
| author |
= |
{Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, |
| title |
= |
{Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours}, |
| year |
= |
{2010}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. IS&T/SPIE Electronic Imaging}, |
| address |
= |
{San Jose, USA}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/46/54/72/PDF/Kulikova_SPIE2010.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics} |
| } |
Abstract :
We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. From an alternative point of view, the approach can be viewed as an extension of the active contour methodology to an a priori unknown number of
objects. Sampling and estimation are based on a stochastic birth-and-death process defined on the configuration space of an arbitrary number of objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data. |
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5 - A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects.
M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IEEE SITIS, Publ. IEEE Computer Society, Marrakech, Maroc, December 2009.
Keywords : Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics.
@INPROCEEDINGS{Kulikova09a,
|
| author |
= |
{Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, |
| title |
= |
{A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. IEEE SITIS}, |
| publisher |
= |
{IEEE Computer Society}, |
| address |
= |
{Marrakech, Maroc}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/43/63/20/PDF/PID1054029.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics} |
| } |
Abstract :
We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple
objects. The single objects considered are defined by both the image data
and the prior information in a way that controls the computational
complexity of the estimation problem. The method is tested via experiments
on a very high resolution aerial image of a scene composed of tree crowns. |
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6 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics.
C. Benedek and X. Descombes and J. Zerubia. In IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, December 2009.
Keywords : Marked point process, Change detection, Aerial images, Building extraction, Satellite images.
@INPROCEEDINGS{benedekWacv09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| booktitle |
= |
{IEEE Workshop on Applications of Computer Vision (WACV)}, |
| pages |
= |
{100-105}, |
| address |
= |
{Snowbird, Utah, USA}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, |
| keyword |
= |
{Marked point process, Change detection, Aerial images, Building extraction, Satellite images} |
| } |
Abstract :
In this paper 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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7 - Object extraction from high resolution SAR images using a birth and death dynamics.
F. Arslan and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009.
Keywords : High resolution SAR images, Object extraction, Marked point process, birth and death process.
@INPROCEEDINGS{Fatih09,
|
| author |
= |
{Arslan, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Object extraction from high resolution SAR images using a birth and death dynamics}, |
| year |
= |
{2009}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Cairo, Egypt}, |
| url |
= |
{http://dx.doi.org/10.1109/ICIP.2009.5413907}, |
| keyword |
= |
{High resolution SAR images, Object extraction, Marked point process, birth and death process} |
| } |
Abstract :
We present a new approach to extract predefined objects, such as trees and oil tanks for instance, from high resolution SAR images. We consider a stochastic approach based on an object process also called marked point process. The objects represent trees or oil tanks which are modeled by disks in the image. We first define a Gibbs density that takes into account both prior information and the data. The energy we define is composed of two terms, one is a prior, penalizing overlaps between objects, and the other is a data term, which measures the suitability of an object in the SAR image. The problem is then reduced to an energy minimization problem. We sample the process to extract the configuration of objects minimizing the energy by a fast birth-and-death dynamics, leading to the total number of objects (trees or oil tanks in our case). This approach is much faster than manual counts and does not need any preprocessing or supervision of a user. |
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8 - 2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong-Kong, August 2006.
Keywords : Data energy, Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process.
@INPROCEEDINGS{perrin_06_c,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes}, |
| year |
= |
{2006}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Hong-Kong}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_perrin_06_c.pdf}, |
| keyword |
= |
{Data energy, Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process} |
| } |
Abstract :
High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the forest resources, from which can be deduced information of biodiversity and ecological sustainability. In that prospect, automatic algorithms are needed to give a further exploitation of the data and to assist human operators. 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 positions of the trees and marks their geometric features. This approach gives also the number of stems, their position, and their size. It is 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. Results are shown on aerial images provided by the French National Forest Inventory (IFN). |
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9 - A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests.
M. Eriksson and G. Perrin and X. Descombes and J. Zerubia. In Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Marne La Vallee, France, July 2006.
Keywords : Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction.
@INPROCEEDINGS{eriksson06a,
|
| author |
= |
{Eriksson, M. and Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_eriksson06a.pdf}, |
| keyword |
= |
{Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction} |
| } |
Abstract :
Most of today's silviculture methods has the goal to optimise the outcome of the forest in stem volume when it is cut. It might also be relevant to save parts of the forest, for instance, to protect a habitat. In order to get a good survey of the forest, remote sensed images are often used. These images are most often manually interpreted in combination with field measurements in order to estimate the forest parameters that are of importance in the decision how to optimally maintain the forest. Among these parameters the most common are stem number, stem volume, and tree species. Interpretation of images are often labour and time consuming. Thus, automatically developed methods for interpretation can lower the work load and speed up the interpretation time.
The interpretation is often done using images captured from a far distance from the ground in order to capture as large area as possible. However, this lower the accuracy of the estimates since it must be done stand wise. Knowledge of where each individual trees in the forest is located together with its size will increase accuracy. It makes it also possible to plan the cutting in detail. With this knowledge in mind, research about finding automatically methods for finding individual tree crowns in aerial images has been a subject for researchers the last decades.
Today's methods are not capable to alone handle all kind of forests. Therefore, comparative studies of different segmentation methods with different types of forests are of importance in order to clarify how much a method is reliable at a certain type of forest. This knowledge can, for instance, be used to build up an expert system which are supposed to be able to find individual tree crowns in any kind of forests. The comparison is done using images covering different types of forests. The types of forests that are included in the study ranges from isolated tree crown where the ground is clearly visible between the crowns to dense forest which is naturally regenerated via planted forest.
In this study we compare three existing segmentation methods for extracting individual tree crowns from aerial images. The first two methods are probabilistic methods which minimises some energy function while the third is a region growing algorithm. The first probabilistic method is based on a Markov Random Field modelling. We define a prior Markov model to segment the image into three classes (background, vegetation and tree centres). The prior model embed a circular shape model of the tree crown with a random radius. The data term allows to well position the tree centres onto the image and to describe the tree shape as fluctuations around the circular template. Besides, some long range interactions models the relations between the trees locations, such as some periodicity in case of plantations.
The second probabilistic method consists in modeling the trees in the forestry images as random configurations of ellipses or ellipsoids, 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. We estimate the best configuration of an unknown number of objects, from which 2D and 3D vegetation resource parameters can be extracted. 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. This approach works well on plantations, where there are high spatial relations between the trees, and on isolated trees where 3D parameters can be extracted, but some difficulties remain in dense areas.
The third method, the region growing algorithm, relies as all region growing methods on good seed points, i.e. in this case approximate locations of the tree crowns. From the seed points the segments are grown according to a grey level value of the neighbouring pixels. The larger the value is the sooner it is connected to the neighbouring segment. The segments stops to grow when all pixels belongs to a segment. This method, contrary the others, will have as a result, segments that have captured the actual shape of the tree crown if the forest is not too sparse. If the forest is too sparse such that the ground is visible, there are problems of finding the seed points. In the cases when the forest is sparse, there are difficulties to separate the tree crowns from the ground. Even if the seed points would be located only at the tree crowns the result will contain a lot of errors since all pixels most belong to a segment, i.e. even the ground pixels must be connected to a segment in this case. |
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10 - Evaluation des Ressources Forestières à l'aide de Processus Ponctuels Marqués.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA), Tours, France, January 2006.
Keywords : Tree Crown Extraction, Stochastic geometry, Marked point process, Object extraction.
@INPROCEEDINGS{perrin_06_a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Evaluation des Ressources Forestières à l'aide de Processus Ponctuels Marqués}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, |
| address |
= |
{Tours, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_rfia06.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Stochastic geometry, Marked point process, Object extraction} |
| } |
Résumé :
Les images aériennes et satellitaires jouent un role de plus en plus important dans le domaine de la gestion des ressources naturelles, et en particulier des forêts. Les organismes chargés d'en faire l'inventaire, comme l'Inventaire Forestier National (IFN) en France, s'appuient en effet sur ces images pour observer les différentes espèces d'arbres d'une zone boisée, avant de se rendre sur le terrain pour une étude plus poussée. La résolution submétrique des données permet, en outre, d'entrevoir une étude plus fine, à savoir un comptage à l'arbre près et une classification automatique des houppiers (ensemble des branches et du feuillage d'un arbre). Cette évaluation précise des ressources forestières n'est actuellement pas disponible. Aussi, le développement d'outils automatiques, chargés d'aider les gestionnaires du paysage dans leur travail en leur apportant une connaissance des ressources à l'échelle de l'arbre, se révèle-t-il être d'un intérêt grandissant.L'objectif de notre travail est donc d'extraire des houppiers à partir d'images aériennes de forêts à très haute résolution. Notre approche consiste à modéliser les peuplements forestiers par un processus ponctuel marqué d'ellipses, dont les points représentent les positions des arbres et les marques leurs caractéristiques géométriques. La densité de ce processus comporte une composante de régularisation, dite a priori, qui introduit des interactions entre les objets du processus, ainsi qu'une composante d'attache aux données, afin que les objets du processus se positionnent sur les houppiers que l'on souhaite extraire. Il s'agit de trouver la configuration d'objets, en nombre inconnu a priori, qui maximise cette densité. La simulation de tels processus fait appel aux algorithmes de type Monte Carlo par Chaîne de Markov (MCMC) à sauts réversibles, l'optimisation étant réalisée à l'aide d'un recuit simulé.Nous présentons ici un nouveau modèle d'attache aux données. Contrairement à nos précédents modèles testés sur des plantations, ce modèle n'est plus bayésien puisque le terme d'attache aux données est désormais calculé au niveau des objets et non de l'image. Ceci nous permet de travailler sur des images plus générales, avec des densités d'arbres plus variables. Des résultats obtenus sur des images fournies par l'IFN valident ce modèle. |
Abstract :
Aerial and satellite imagery has a key role to play in natural resources management, especially in forestry application. Indeed, forest inventories, such as the French National Inventory (IFN), refer to these images to analyse the different tree species in a stand, before sending a team on the ground to obtain some more advanced knowledge. Moreover, the submetric resolution of the data enables to study forests at the scale of trees, and also to get a more accurate evaluation of the resources such as the number of stems. It would be also of important economical and environmental concerns to develop automatic tools to analyze and monitor forests.We aim at extracting tree crowns from high resolution aerial images of forests. Our approach consists in modelling the forestry images as realizations of a marked point process of ellipses, whose points are the positions of the trees 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 the marked point process, we use Monte Carlo dynamics (Reversible Jump Markov Chain Monte Carlo), while the optimization is performed via a simulated annealing algorithm.We present here a new model for the data term. Contrary to our previous models tested on plantations images, this model is not Bayesian anymore : the data term is calculated for each object and not for the whole image. This enables us to work on more general images, with variable tree crown densities. Example results are shown on aerial images provided by the French Forest Inventory (IFN). |
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