The Publications

Result of the query in the list of publications :

• 101 Articles
• 31 PhD Thesis and Habilitations
• 245 Conference articles
• 90 Technical and Research Reports
• 14 Collection articles or Books chapters
• 5 Books

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245 Conference articles

109 - Vers une détection et une classification non-supervisées des changements inter-images. A. Fournier and X. Descombes and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), 2007. Keywords : Markov Random Fields, Registration, Change detection, Clustering. @INPROCEEDINGS{fournier-taima-07, author = {Fournier, A. and Descombes, X. and Zerubia, J.}, title = {Vers une détection et une classification non-supervisées des changements inter-images}, year = {2007}, booktitle = {Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_fournier-taima-07.pdf}, keyword = {Markov Random Fields, Registration, Change detection, Clustering} }

110 - Use of ant colony optimization for finding neighbourhoods in non-stationary Markov random field models. S. Le Hegarat-Mascle and A. Kallel and X. Descombes. In Pattern Recognition and Machine Intelligence (PReMI'07), 2007. Keywords : Ants colonization, Markov Random Fields. @INPROCEEDINGS{ant07b, author = {Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, title = {Use of ant colony optimization for finding neighbourhoods in non-stationary Markov random field models}, year = {2007}, booktitle = {Pattern Recognition and Machine Intelligence (PReMI'07)}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_ant07b.pdf}, keyword = {Ants colonization, Markov Random Fields} }

111 - Tree Species Classification Using Radiometry, Texture and Shape Based Features. M. S. Kulikova and M. Mani and A. Srivastava and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), 2007. Keywords : shape based features, SVM, tree classification. @INPROCEEDINGS{Kulikova07, author = {Kulikova, M. S. and Mani, M. and Srivastava, A. and Descombes, X. and Zerubia, J.}, title = {Tree Species Classification Using Radiometry, Texture and Shape Based Features}, year = {2007}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, pdf = {http://hal.archives-ouvertes.fr/docs/00/46/55/05/PDF/Kulikova_EUSIPCO2007.pdf}, keyword = {shape based features, SVM, tree classification} } Abstract : We consider the problem of tree species classification from high resolution aerial images based on radiometry, texture and a shape modeling. We use the notion of shape space proposed by Klassen et al., which provides a shape description invariant to translation, rotation and scaling. The shape features are extracted within a geodesic distance in the shape space. We then perform a classification using a SVM approach. We are able to show that the shape descriptors improve the classification performance relative to a classifier based on radiometric and textural descriptors alone. We obtain these results using high resolution Colour InfraRed (CIR) aerial images provided by the Swedish University of Agricultural Sciences. The image viewpoint is close to the nadir, i.e. the tree crowns are seen from above.

112 - An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP), Madurai, India, December 2006. Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape. @INPROCEEDINGS{Horvath06_icvgip, author = {Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, title = {An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction}, year = {2006}, month = {December}, booktitle = {Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP)}, address = {Madurai, India}, url = {http://dx.doi.org/10.1007/11949619_14}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath06_icvgip.pdf}, keyword = {Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} } Abstract : A central task in image processing is to find the region in the image corresponding to an entity. In a number of problems, the region takes the form of a collection of circles, eg tree crowns in remote sensing imagery; cells in biological and medical imagery. In~citeHorvath06b, a model of such regions, the `gas of circles' model, was developed based on higher-order active contours, a recently developed framework for the inclusion of prior knowledge in active contour energies. However, the model suffers from a defect. In~citeHorvath06b, the model parameters were adjusted so that the circles were local energy minima. Gradient descent can become stuck in these minima, producing phantom circles even with no supporting data. We solve this problem by calculating, via a Taylor expansion of the energy, parameter values that make circles into energy inflection points rather than minima. As a bonus, the constraint halves the number of model parameters, and severely constrains one of the two that remain, a major advantage for an energy-based model. We use the model for tree crown extraction from aerial images. Experiments show that despite the lack of parametric freedom, the new model performs better than the old, and much better than a classical active contour.

113 - Burnt area mapping using Support Vector Machines. O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Forest Fire Research, Figueira da Foz, Portugal, November 2006. Keywords : Satellite images, Forest fires, Burnt areas, Support Vector Machines. @INPROCEEDINGS{zammit_icffr_06, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Burnt area mapping using Support Vector Machines}, year = {2006}, month = {November}, booktitle = {Proc. International Conference on Forest Fire Research}, address = {Figueira da Foz, Portugal}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_zammit_icffr_06.pdf}, keyword = {Satellite images, Forest fires, Burnt areas, Support Vector Machines} }

114 - 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 @INPROCEEDINGS{lafarge_icip06, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images}, year = {2006}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Atlanta}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_icip06.pdf}, keyword = {3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images} }

115 - Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval. A. Bhattacharya and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. compImage, Coimbra, Portugal, October 2006. Keywords : Road network, Indexation, Semantic, Retrieval, Feature statistics. @INPROCEEDINGS{bhatta_compimage06, author = {Bhattacharya, A. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, title = {Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval}, year = {2006}, month = {October}, booktitle = {Proc. compImage}, address = {Coimbra, Portugal}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_bhatta_compimage06.pdf}, keyword = {Road network, Indexation, Semantic, Retrieval, Feature statistics} } Abstract : Retrieval from remote sensing image archives relies on the extraction of pertinent information from the data about the entity of interest (e.g. land cover type), and on the robustness of this extraction to nuisance variables (e.g. illumination). Most image-based characterizations are not invariant to such variables. However, other semantic entities in the image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. Road networks are one example: their properties vary considerably, for example, from urban to rural areas. This paper takes the first steps towards classification (and hence retrieval) based on this idea. We study the dependence of a number of network features on the class of the image ('urban' or 'rural'). The chosen features include measures of the network density, connectedness, and `curviness'. The feature distributions of the two classes are well separated in feature space, thus providing a basis for retrieval. Classification using kernel k-means confirms this conclusion.

116 - Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture. J. Aubray and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, September 2006. Keywords : Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics. @INPROCEEDINGS{aubray_eusipco06, author = {Aubray, J. and Jermyn, I. H. and Zerubia, J.}, title = {Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture}, year = {2006}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Florence, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_aubray_eusipco06.pdf}, keyword = {Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics} } Abstract : Probabilistic adaptive wavelet packet models of texture pro- vide new insight into texture structure and statistics by focus- ing the analysis on significant structure in frequency space. In very adapted subbands, they have revealed new bimodal statistics, corresponding to the structure inherent to a texture, and strong dependencies between such bimodal sub- bands, related to phase coherence in a texture. Existing models can capture the former but not the latter. As a first step to- wards modelling the joint statistics, and in order to simplify earlier approaches, we introduce a new parametric family of models capable of modelling both bimodal and unimodal subbands, and of being generalized to capture the joint statistics. We show how to compute MAP estimates for the adaptive basis and model parameters, and apply the models to Brodatz textures to illustrate their performance.

117 - 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).

118 - A Higher-Order Active Contour Model for Tree Detection. P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong Kong, August 2006. Keywords : Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction. @INPROCEEDINGS{horvath_icpr06, author = {Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, title = {A Higher-Order Active Contour Model for Tree Detection}, year = {2006}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Hong Kong}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_horvath_icpr06.pdf}, keyword = {Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction} } Abstract : We present a model of a ‘gas of circles’, the ensemble of regions in the image domain consisting of an unknown number of circles with approximately fixed radius and short range repulsive interactions, and apply it to the extraction of tree crowns from aerial images. The method uses the re- cently introduced ‘higher order active contours’ (HOACs), which incorporate long-range interactions between contour points, and thereby include prior geometric information without using a template shape. This makes them ideal when looking for multiple instances of an entity in an image. We study an existing HOAC model for networks, and show via a stability calculation that circles stable to perturbations are possible for constrained parameter sets. Combining this prior energy with a data term, we show results on aerial imagery that demonstrate the effectiveness of the method and the need for prior geometric knowledge. The model has many other potential applications.

119 - Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS), Marne La Vallee, France, July 2006. Keywords : 3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations. @INPROCEEDINGS{lafarge_isprs06, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations}, year = {2006}, month = {July}, booktitle = {Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS)}, address = {Marne La Vallee, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_isprs06.pdf}, keyword = {3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations} }

120 - 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.‭

121 - An Automatic 3D City Model : a Bayesian Approach using Satellite Images. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 2006. Note : Copyright IEEE Keywords : 3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM). @INPROCEEDINGS{florenticassp06, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {An Automatic 3D City Model : a Bayesian Approach using Satellite Images}, year = {2006}, month = {May}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Toulouse, France}, note = {Copyright IEEE}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_florenticassp06.pdf}, keyword = {3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM)} }

122 - 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. @INPROCEEDINGS{perrin_06_b, author = {Perrin, G. and Descombes, X. and Zerubia, J. and Boureau, J.G.}, title = {Forest Resource Assessment using Stochastic Geometry}, year = {2006}, month = {March}, booktitle = {Proc. International Precision Forestry Symposium}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_ipfs06.pdf}, keyword = {Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy} } 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)

123 - A study of Gaussian approximations of fluorescence microscopy PSF models. B. Zhang and J. Zerubia and J.C. Olivo-Marin. In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press, Vol. 6090, San Jose, USA, January 2006. Copyright : SPIE @INPROCEEDINGS{zerubia_spie06, author = {Zhang, B. and Zerubia, J. and Olivo-Marin, J.C.}, title = {A study of Gaussian approximations of fluorescence microscopy PSF models}, year = {2006}, month = {January}, booktitle = {Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press}, volume = {6090}, address = {San Jose, USA}, keyword = {} }

124 - 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).

125 - 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} }

126 - Point process of segments and rectangles for building extraction from DEM. M. Ortner and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006. Keywords : Stochastic geometry, Buildings. @INPROCEEDINGS{ortner06, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {Point process of segments and rectangles for building extraction from DEM}, year = {2006}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_ortner06.pdf}, keyword = {Stochastic geometry, Buildings} }

127 - 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.

128 - Phase field models and higher-order active contours. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005. Keywords : Active contour, Higher-order, Shape, Line networks, Road network, Phase Field. @INPROCEEDINGS{rochery_iccv05, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Phase field models and higher-order active contours}, year = {2005}, month = {October}, booktitle = {Proc. IEEE International Conference on Computer Vision (ICCV)}, address = {Beijing, China}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_iccv05.pdf}, keyword = {Active contour, Higher-order, Shape, Line networks, Road network, Phase Field} } Abstract : The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a `phase field' function. The function is not constrained, eg to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce `higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points. In addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images.

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