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• 245 Articles de conférence

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245 Articles de conférence

101 - A Hierarchical finite-state model for texture segmentation. G. Scarpa et M. Haindl et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, pages 1209-1212, Honolulu, HI (USA), avril 2007. @INPROCEEDINGS{scarpa_icassp_07, author = {Scarpa, G. and Haindl, M. and Zerubia, J.}, title = {A Hierarchical finite-state model for texture segmentation}, year = {2007}, month = {avril}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, volume = {1}, pages = {1209-1212}, address = {Honolulu, HI (USA)}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4217303}, keyword = {} }

102 - Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry. T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. Urban, Paris, France, avril 2007. Mots-clés : Reseaux routiers, Very high resolution, Multiscale, Ordre superieur, Contour actif, Forme. @INPROCEEDINGS{Peng07_urban, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = {Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry}, year = {2007}, month = {avril}, booktitle = {Proc. Urban}, address = {Paris, France}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Peng07urban.pdf}, keyword = {Reseaux routiers, Very high resolution, Multiscale, Ordre superieur, Contour actif, Forme} } Abstract : This paper addresses the problem of automatically extracting the main road network in a dense urban area from a very high resolution optical satellite image using a variational approach. The model energy has two parts: a phase field higherorder active contour energy that describes our prior knowledge of road network geometry, i.e. that it is composed of elongated structures with roughly parallel borders that meet at junctions; and a multi-scale statistical image model describing the image we expect to see given a road network. By minimizing the model energy, an estimate of the road network is obtained. Promising results on 0.6m QuickBird Panchromatic images are presented, and future improvements to the models are outlined.

103 - Image deconvolution using a stochastic differential equation approach. X. Descombes et M. Lebellego et E. Zhizhina. Dans Proc. nternational Conference on Computer Vision Theory and Applications, Barcelona, Spain, mars 2007. Mots-clés : Deconvolution, Stochastic Differential Equation. @INPROCEEDINGS{xavBarca2, author = {Descombes, X. and Lebellego, M. and Zhizhina, E.}, title = {Image deconvolution using a stochastic differential equation approach}, year = {2007}, month = {mars}, booktitle = {Proc. nternational Conference on Computer Vision Theory and Applications}, address = {Barcelona, Spain}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_xavBarca2.pdf}, keyword = {Deconvolution, Stochastic Differential Equation} }

104 - Circular object segmentation using higher-order active contours. P. Horvath et I. H. Jermyn et Z. Kato et J. Zerubia. Dans In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07), Debrecen, Hungary, janvier 2007. Note : In Hungarian Mots-clés : Ordre superieur, Extraction de Houppiers, Forme. @INPROCEEDINGS{Horvath07a, author = {Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, title = {Circular object segmentation using higher-order active contours}, year = {2007}, month = {janvier}, booktitle = {In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07)}, address = {Debrecen, Hungary}, note = {In Hungarian}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07a.pdf}, keyword = {Ordre superieur, Extraction de Houppiers, Forme} }

105 - Gap Filling in 3D Vessel like Patterns with Tensor Fields. L. Risser et F. Plouraboue et X. Descombes. Dans Proc. International Conference on Computer Vision Theory and Applications, 2007. Mots-clés : tensor voting, vascular network. @INPROCEEDINGS{XDbarca1, author = {Risser, L. and Plouraboue, F. and Descombes, X.}, title = {Gap Filling in 3D Vessel like Patterns with Tensor Fields}, year = {2007}, booktitle = {Proc. International Conference on Computer Vision Theory and Applications}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_XDbarca1.pdf}, keyword = {tensor voting, vascular network} }

106 - Wavelet-based restoration methods: application to 3D confocal microscopy images. C. Chaux et L. Blanc-Féraud et J. Zerubia. Dans Proc. SPIE Conference on Wavelets, 2007. Mots-clés : Restauration, Deconvolution, 3D images, Microscopie confocale, Poisson noise, Ondelettes. Copyright : Copyright 2007 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proc. SPIE Conference on Wavelets and is made available as an electronic reprint (preprint) with permission of SPIE. 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{chaux2007, author = {Chaux, C. and Blanc-Féraud, L. and Zerubia, J.}, title = {Wavelet-based restoration methods: application to 3D confocal microscopy images}, year = {2007}, booktitle = {Proc. SPIE Conference on Wavelets}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_chaux2007.pdf}, keyword = {Restauration, Deconvolution, 3D images, Microscopie confocale, Poisson noise, Ondelettes} }

107 - Detection and Completion of Filaments: A Vector Field and PDE Approach. A. Baudour et G. Aubert et L. Blanc-Féraud. Dans SSVM 2007, LNCS 4485 proceedings, 2007. @INPROCEEDINGS{ssvm2007, author = {Baudour, A. and Aubert, G. and Blanc-Féraud, L.}, title = {Detection and Completion of Filaments: A Vector Field and PDE Approach}, year = {2007}, booktitle = { SSVM 2007, LNCS 4485 proceedings}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_ssvm2007.pdf}, keyword = {} }

108 - Détection et Complétion de Filaments: une approche variationelle et vectorielle. A. Baudour et G. Aubert et L. Blanc-Féraud. Dans Colloque Gretsi Troyes, 2007, 2007. @INPROCEEDINGS{ Gretsi 2007, author = {Baudour, A. and Aubert, G. and Blanc-Féraud, L.}, title = {Détection et Complétion de Filaments: une approche variationelle et vectorielle}, year = {2007}, booktitle = {Colloque Gretsi Troyes, 2007}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_ Gretsi 2007.pdf}, keyword = {} }

109 - Vers une détection et une classification non-supervisées des changements inter-images. A. Fournier et X. Descombes et J. Zerubia. Dans Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), 2007. Mots-clés : 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 et A. Kallel et X. Descombes. Dans Pattern Recognition and Machine Intelligence (PReMI'07), 2007. Mots-clés : 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 et M. Mani et A. Srivastava et X. Descombes et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), 2007. Mots-clés : 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 et I. H. Jermyn et Z. Kato et J. Zerubia. Dans Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP), Madurai, India, décembre 2006. Mots-clés : Extraction de Houppiers, Aerial images, Ordre superieur, Contour actif, Gaz de cercles, Forme. @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 = {décembre}, 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 = {Extraction de Houppiers, Aerial images, Ordre superieur, Contour actif, Gaz de cercles, Forme} } 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 et X. Descombes et J. Zerubia. Dans Proc. International Conference on Forest Fire Research, Figueira da Foz, Portugal, novembre 2006. Mots-clés : Imagerie satellitaire, Feux de foret, Zones brûlées, 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 = {novembre}, 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 = {Imagerie satellitaire, Feux de foret, Zones brûlées, Support Vector Machines} }

114 - An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images. F. Lafarge et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Dans Proc. IEEE International Conference on Image Processing (ICIP), Atlanta, octobre 2006. Mots-clés : Reconstruction en 3D, Batiments, RJMCMC, Approche structurelle, Imagerie satellitaire. 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 = {octobre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Atlanta}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_icip06.pdf}, keyword = {Reconstruction en 3D, Batiments, RJMCMC, Approche structurelle, Imagerie satellitaire} }

115 - Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval. A. Bhattacharya et I. H. Jermyn et X. Descombes et J. Zerubia. Dans Proc. compImage, Coimbra, Portugal, octobre 2006. Mots-clés : Reseaux routiers, Indexation, Semantique, 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 = {octobre}, booktitle = {Proc. compImage}, address = {Coimbra, Portugal}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_bhatta_compimage06.pdf}, keyword = {Reseaux routiers, Indexation, Semantique, 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 et I. H. Jermyn et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, septembre 2006. Mots-clés : Texture, Adaptatif, Paquet d'ondelettes, Nonlineaire, Bimodale, 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 = {septembre}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Florence, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_aubray_eusipco06.pdf}, keyword = {Texture, Adaptatif, Paquet d'ondelettes, Nonlineaire, Bimodale, 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 et X. Descombes et J. Zerubia. Dans Proc. International Conference on Pattern Recognition (ICPR), Hong-Kong, août 2006. Mots-clés : Energie d'attache aux données, Extraction d'objets, Extraction de Houppiers, Geometrie stochastique, Processus ponctuels marques. @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 = {août}, 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 = {Energie d'attache aux données, Extraction d'objets, Extraction de Houppiers, Geometrie stochastique, Processus ponctuels marques} } 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 et I. H. Jermyn et Z. Kato et J. Zerubia. Dans Proc. International Conference on Pattern Recognition (ICPR), Hong Kong, août 2006. Mots-clés : Contour actif, Gaz de cercles, Ordre superieur, Forme, A priori, Extraction de Houppiers. @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 = {août}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Hong Kong}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_horvath_icpr06.pdf}, keyword = {Contour actif, Gaz de cercles, Ordre superieur, Forme, A priori, Extraction de Houppiers} } 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 et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Dans Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS), Marne La Vallee, France, juillet 2006. Mots-clés : Reconstruction en 3D, Digital Elevation Model, Building extraction, Zones urbaines denses, 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 = {juillet}, 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 = {Reconstruction en 3D, Digital Elevation Model, Building extraction, Zones urbaines denses, PLEIADES simulations} }

120 - A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests. M. Eriksson et G. Perrin et X. Descombes et J. Zerubia. Dans Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Marne La Vallee, France, juillet 2006. Mots-clés : Croissance de Region, Processus ponctuels marques, Champs de Markov, Extraction d'objets, Extraction de Houppiers. @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 = {juillet}, 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 = {Croissance de Region, Processus ponctuels marques, Champs de Markov, Extraction d'objets, Extraction de Houppiers} } 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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