Publications de Ian Jermyn

Résultat de la recherche dans la liste des publications :

• 12 Articles
• 46 Articles de conférence
• 9 Rapports de recherche et Rapports techniques
• 2 Articles de collection ou Chapitres de livres

nouvelle recherche | bibtex

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12 Articles

1 - A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images. M. S. Kulikova et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. International Journal of Computer Vision and Image Processing, 1(2): pages 1-12, 2011. Mots-clés : Contour actif, Processus ponctuels marques, multiple birth-and-death dynamics, multiple object extraction, Shape prior. @ARTICLE{kulikova_ijcvip2010, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images}, year = {2011}, journal = {International Journal of Computer Vision and Image Processing}, volume = {1}, number = {2}, pages = {1-12}, url = {http://hal.archives-ouvertes.fr/hal-00804118}, keyword = {Contour actif, Processus ponctuels marques, multiple birth-and-death dynamics, multiple object extraction, Shape prior} } Abstract : Object extraction from images is one of the most important tasks in remote sensing image analysis. For accurate extraction from very high resolution (VHR) images, object geometry needs to be taken into account. A method for incorporating strong yet flexible prior shape information into a marked point process model for the extraction of multiple objects of complex shape is presented. To control the computational complexity, the objects considered are defined using the image data and the prior shape information. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process on the space of multiple objects. The authors present several experimental results on the extraction of tree crowns from VHR aerial images.

2 - Extended Phase Field Higher-Order Active Contour Models for Networks. T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. International Journal of Computer Vision, 88(1): pages 111-128, mai 2010. Mots-clés : Contour actif, Champ de Phase, Shape prior, Parameter analysis, remote sensing, Road network extraction. @ARTICLE{Peng09, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = { Extended Phase Field Higher-Order Active Contour Models for Networks}, year = {2010}, month = {mai}, journal = {International Journal of Computer Vision}, volume = {88}, number = {1}, pages = { 111-128}, url = {http://www.springerlink.com/content/d3641g2227316w58/}, keyword = {Contour actif, Champ de Phase, Shape prior, Parameter analysis, remote sensing, Road network extraction} } Abstract : This paper addresses the segmentation from an image of entities that have the form of a ‘network’, i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present new phase field higher-order active contour (HOAC) prior models for network regions, and apply them to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much ‘noise’ in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular.

3 - Shape Analysis of Elastic Curves in Euclidean Spaces. S. Joshi et E. Klassen et W. Liu et I. H. Jermyn et A. Srivastava. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(7): pages 1415-1428, 2010. Note : to appear Mots-clés : shape analysis, elastic deformations, Riemannian elastic metric. @ARTICLE{Joshi2010, author = {Joshi, S. and Klassen, E. and Liu, W. and Jermyn, I. H. and Srivastava, A.}, title = {Shape Analysis of Elastic Curves in Euclidean Spaces}, year = {2010}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {33}, number = {7}, pages = {1415-1428}, note = {to appear}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5601739}, keyword = {shape analysis, elastic deformations, Riemannian elastic metric} }

4 - Looking for shapes in two-dimensional, cluttered point clouds. A. Srivastava et I. H. Jermyn. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(9): pages 1616-1629, septembre 2009. Mots-clés : Forme, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @ARTICLE{SrivastavaJermyn09, author = {Srivastava, A. and Jermyn, I. H.}, title = {Looking for shapes in two-dimensional, cluttered point clouds}, year = {2009}, month = {septembre}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {31}, number = {9}, pages = {1616-1629}, url = {http://dx.doi.org/10.1109/TPAMI.2008.223}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/SrivastavaJermyn09.pdf}, keyword = {Forme, Bayesian, Point cloud, Diffeomorphism, Sampling, Fisher-Rao} } Abstract : We study the problem of identifying shape classes in point clouds. These clouds contain sampled contours and are corrupted by clutter and observation noise. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of sampled contours using models learnt from training data to evaluate the given test data. To facilitate simulations, we develop statistical models for sources of (nuisance) variability: (i) shape variations within classes, (ii) variability in sampling continuous curves, (iii) pose and scale variability, (iv) observation noise, and (v) points introduced by clutter. The variability in sampling closed curves into finite points is represented by positive diffeomorphisms of a unit circle. We derive probability models on these functions using their squareroot forms and the Fisher-Rao metric. Using a Monte Carlo approach, we simulate configurations from a joint prior on the shape-sample space and compare them to the data using a likelihood function. Average likelihoods of simulated configurations lead to estimates of posterior probabilities of different classes and, hence, Bayesian classification.

5 - A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction. P. Horvath et I. H. Jermyn et Z. Kato et J. Zerubia. Pattern Recognition, 42(5): pages 699-709, mai 2009. Mots-clés : Forme, Ordre superieur, Contour actif, Gaz de cercles, Extraction de Houppiers, Bayesian. @ARTICLE{Horvath09, author = {Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, title = {A higher-order active contour model of a ‘gas of circles' and its application to tree crown extraction}, year = {2009}, month = {mai}, journal = {Pattern Recognition}, volume = {42}, number = {5}, pages = {699-709}, url = {http://dx.doi.org/10.1016/j.patcog.2008.09.008}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Horvathetal09.pdf}, keyword = {Forme, Ordre superieur, Contour actif, Gaz de cercles, Extraction de Houppiers, Bayesian} } Abstract : We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown number of circles of approximately the same radius. The model has applications to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed using higher-order active contours (HOACs) in order to include non-trivial prior knowledge about region shape without constraining topology. The main theoretical contribution is an analysis of the local minima of the HOAC energy that allows us to guarantee stable circles, fix one of the model parameters, and constrain the rest. We apply the model to tree crown extraction from aerial images of plantations. Numerical experiments both confirm the theoretical analysis and show the empirical importance of the prior shape information.

6 - Incorporating generic and specific prior knowledge in a multi-scale phase field model for road extraction from VHR images. T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 1(2): pages 139--146, juin 2008. Mots-clés : Zones urbaines denses, Système d'Information Géographique (SIG), Multiscale, Reseaux routiers, Methodes variationnelles, Very high resolution. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @ARTICLE{Peng08b, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = {Incorporating generic and specific prior knowledge in a multi-scale phase field model for road extraction from VHR images}, year = {2008}, month = {juin}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {1}, number = {2}, pages = {139--146}, url = {http://dx.doi.org/10.1109/JSTARS.2008.922318}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/PengetalTGRS08.pdf}, keyword = {Zones urbaines denses, Système d'Information Géographique (SIG), Multiscale, Reseaux routiers, Methodes variationnelles, Very high resolution} } Abstract : This paper addresses the problem of updating digital road maps in dense urban areas by extracting the main road network from very high resolution (VHR) satellite images. Building on the work of Rochery et al. (2005), we represent the road region as a 'phase field'. In order to overcome the difficulties due to the complexity of the information contained in VHR images, we propose a multi-scale statistical data model. It enables the integration of segmentation results from coarse resolution, which furnishes a simplified representation of the data, and fine resolution, which provides accurate details. Moreover, an outdated GIS digital map is introduced into the model, providing specific prior knowledge of the road network. This new term balances the effect of the generic prior knowledge describing the geometric shape of road networks (i.e. elongated and of low-curvature) carried by a 'phase field higher-order active contour' term. Promising results on QuickBird panchromatic images and comparisons with several other methods demonstrate the effectiveness of our approach.

7 - Higher-Order Active Contour Energies for Gap Closure. M. Rochery et I. H. Jermyn et J. Zerubia. Journal of Mathematical Imaging and Vision, 29(1): pages 1-20, septembre 2007. Mots-clés : Gap closure, Ordre superieur, Contour actif, Forme, A priori, Reseaux routiers. @ARTICLE{Rochery07, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher-Order Active Contour Energies for Gap Closure}, year = {2007}, month = {septembre}, journal = {Journal of Mathematical Imaging and Vision}, volume = {29}, number = {1}, pages = {1-20}, url = {http://dx.doi.org/10.1007/s10851-007-0021-x}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Rochery07.pdf}, keyword = {Gap closure, Ordre superieur, Contour actif, Forme, A priori, Reseaux routiers} } Abstract : One of the main difficulties in extracting line networks from images, and in particular road networks from remote sensing images, is the existence of interruptions in the data caused, for example, by occlusions. These can lead to gaps in the extracted network that do not correspond to gaps in the real network. In this paper, we describe a higher-order active contour energy that in addition to favouring network-like regions, includes a prior term penalizing networks containing ‘nearby opposing extremities’, thereby making gaps in the extracted network less likely. The new energy term causes such extremities to attract one another during gradient descent. They thus move towards one another and join, closing the gap. To minimize the energy, we develop specific techniques to handle the high-order derivatives that appear in the gradient descent equation. We present the results of automatic extraction of networks from real remote-sensing images, showing the ability of the model to overcome interruptions.

8 - Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images. A. Bhattacharya et M. Roux et H. Maitre et I. H. Jermyn et X. Descombes et J. Zerubia. International Journal of Simulation Modelling, 6(2): pages 73--83, 2007. @ARTICLE{Bhattacharya07, author = {Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, title = {Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images}, year = {2007}, journal = {International Journal of Simulation Modelling}, volume = {6}, number = {2}, pages = {73--83}, url = {http://www.ijsimm.com/Full_Papers/Fulltext2007/text6-2_73-83.pdf}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07.pdf}, keyword = {} } Abstract : Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database.

9 - Higher Order Active Contours. M. Rochery et I. H. Jermyn et J. Zerubia. International Journal of Computer Vision, 69(1): pages 27--42, août 2006. Mots-clés : Contour actif, Forme, Ordre superieur, A priori, Reseaux routiers. @ARTICLE{mr_ijcv_06, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Higher Order Active Contours}, year = {2006}, month = {août}, journal = {International Journal of Computer Vision}, volume = {69}, number = {1}, pages = {27--42}, url = {http://dx.doi.org/10.1007/s11263-006-6851-y}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_mr_ijcv_06.pdf}, keyword = {Contour actif, Forme, Ordre superieur, A priori, Reseaux routiers} } Abstract : We introduce a new class of active contour models that hold great promise for region and shape modelling, and we apply a special case of these models to the extraction of road networks from satellite and aerial imagery. The new models are arbitrary polynomial functionals on the space of boundaries, and thus greatly generalize the linear functionals used in classical contour energies. While classical energies are expressed as single integrals over the contour, the new energies incorporate multiple integrals, and thus describe long-range interactions between different sets of contour points. As prior terms, they describe families of contours that share complex geometric properties, without making reference to any particular shape, and they require no pose estimation. As likelihood terms, they can describe multi-point interactions between the contour and the data. To optimize the energies, we use a level set approach. The forces derived from the new energies are non-local however, thus necessitating an extension of standard level set methods. Networks are a shape family of great importance in a number of applications, including remote sensing imagery. To model them, we make a particular choice of prior quadratic energy that describes reticulated structures, and augment it with a likelihood term that couples the data at pairs of contour points to their joint geometry. Promising experimental results are shown on real images.

10 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation. H. Permuter et J.M. Francos et I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, avril 2006. Mots-clés : Classification, Segmentation, Texture, Couleur, Mixture de gaussiennes, Decison fusion. @ARTICLE{permuter_pr06, author = {Permuter, H. and Francos, J.M. and Jermyn, I. H.}, title = {A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, year = {2006}, month = {avril}, journal = {Pattern Recognition}, volume = {39}, number = {4}, pages = {695--706}, url = {http://dx.doi.org/10.1016/j.patcog.2005.10.028}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_permuter_pr06.pdf}, keyword = {Classification, Segmentation, Texture, Couleur, Mixture de gaussiennes, Decison fusion} } Abstract : The aims of this paper are two-fold: to define Gaussian mixture models of coloured texture on several feature paces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct Gaussian mixtures models over a variety of different colour and texture feature spaces, with a view to the retrieval of textured colour images from databases. We compare supervised classification results for different choices of colour and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'colour' and 'structure' information in order to improve the classification performance. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.

11 - Invariant Bayesian estimation on manifolds. I. H. Jermyn. Annals of Statistics, 33(2): pages 583--605, avril 2005. Mots-clés : Estimation bayesienne, MAP, MMSE, Invariant, Metrique, Jeffrey's. @ARTICLE{jermyn_annstat05, author = {Jermyn, I. H.}, title = {Invariant Bayesian estimation on manifolds}, year = {2005}, month = {avril}, journal = {Annals of Statistics}, volume = {33}, number = {2}, pages = {583--605}, url = {http://dx.doi.org/10.1214/009053604000001273}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_annstat05.pdf}, keyword = {Estimation bayesienne, MAP, MMSE, Invariant, Metrique, Jeffrey's} } Abstract : A frequent and well-founded criticism of the maximum em a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter param taking values in a differentiable manifold paramspace is that they are not invariant to arbitrary `reparametrizations' of paramspace. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a em sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and then provides a solution. We first show that the presence of a metric structure on paramspace can be used to define coordinate-invariant MAP and MMSE estimates, and we argue that this is the natural way to proceed. We then discuss the choice of a metric structure on paramspace. By imposing an invariance criterion natural within a Bayesian framework, we show that this choice is essentially unique. It does not necessarily correspond to a choice of coordinates. In cases of complete prior ignorance, when Jeffreys' prior is used, the invariant MAP estimate reduces to the maximum likelihood estimate. The invariant MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation.

12 - Globally optimal regions and boundaries as minimum ratio weight cycles. I. H. Jermyn et H. Ishikawa. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(10): pages 1075-1088, octobre 2001. Mots-clés : Graphe, Ratio, Cycle, Segmentation, Minimum global. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @ARTICLE{jermyn_tpami01, author = {Jermyn, I. H. and Ishikawa, H.}, title = {Globally optimal regions and boundaries as minimum ratio weight cycles}, year = {2001}, month = {octobre}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {23}, number = {10}, pages = {1075-1088}, url = {http://dx.doi.org/10.1109/34.954599}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_tpami01.pdf}, keyword = {Graphe, Ratio, Cycle, Segmentation, Minimum global} } Abstract : We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,ldots), em and from the interior of the region (texture, homogeneity,ldots). We describe two polynomial-time digraph algorithms for finding the em global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 times 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization.

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46 Articles de conférence

1 - A theoretical and numerical study of a phase field higher-order active contour model of directed networks. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans The Tenth Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand, novembre 2010. Mots-clés : Champ de Phase, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing. Copyright : Springer-Verlag GmbH Berlin Heidelberg @INPROCEEDINGS{Elghoul10b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A theoretical and numerical study of a phase field higher-order active contour model of directed networks}, year = {2010}, month = {novembre}, booktitle = {The Tenth Asian Conference on Computer Vision (ACCV)}, address = {Queenstown, New Zealand}, pdf = {http://hal.archives-ouvertes.fr/inria-00522443/fr/}, keyword = {Champ de Phase, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing} } Abstract : We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest.We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images.

2 - Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, septembre 2010. Mots-clés : Champ de Phase, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing. Copyright : ISPRS @INPROCEEDINGS{Elghoul10a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model}, year = {2010}, month = {septembre}, booktitle = {Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, address = {Paris, France}, pdf = {https://hal.inria.fr/inria-00491017}, keyword = {Champ de Phase, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing} } Abstract : We propose a new algorithm for network segmentation from VHR remote sensing images. The algorithm performs this task quasi-automatically, that is, with no human intervention except to fix some parameters. The task is made difficult by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which branches do not come to an end, hence tending to close gaps in the network; and in which junctions show approximate ‘conservation of width’. We also introduce image models for network and background, which are validated using maximum likelihood segmentation against other possibilities. We then test the full model on VHR optical and multispectral satellite images.

3 - Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours. M. S. Kulikova et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. Dans Proc. IS&T/SPIE Electronic Imaging, San Jose, USA, janvier 2010. Mots-clés : Extraction d'objets, Processus ponctuels marques, Shape prior, Contour actif, 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 = {janvier}, 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 = {Extraction d'objets, Processus ponctuels marques, Shape prior, Contour actif, 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.

4 - A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects. M. S. Kulikova et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. Dans Proc. IEEE SITIS, Publ. IEEE Computer Society, Marrakech, Maroc, décembre 2009. Mots-clés : Extraction d'objets, Processus ponctuels marques, Shape prior, Contour actif, 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 = {décembre}, 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 = {Extraction d'objets, Processus ponctuels marques, Shape prior, Contour actif, 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.

5 - A markov random field model for extracting near-circular shapes. T. Blaskovics et Z. Kato et I. H. Jermyn. Dans Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, novembre 2009. Mots-clés : Segmentation, Markov Random Fields, Shape prior. @INPROCEEDINGS{Blaskovics09, author = {Blaskovics, T. and Kato, Z. and Jermyn, I. H.}, title = {A markov random field model for extracting near-circular shapes}, year = {2009}, month = {novembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5413472}, keyword = {Segmentation, Markov Random Fields, Shape prior} }

6 - A phase field higher-order active contour model of directed networks. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans 2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV, Kyoto, Japan, septembre 2009. Mots-clés : Geometric prior, Forme, Higher-order actif contours, Champ de Phase, Directed networks. Copyright : ©2009 IEEE. @INPROCEEDINGS{ElGhoul09b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A phase field higher-order active contour model of directed networks}, year = {2009}, month = {septembre}, booktitle = {2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV}, address = {Kyoto, Japan}, url = {https://hal.inria.fr/inria-00409910}, pdf = {http://hal.inria.fr/docs/00/40/99/10/PDF/nordia09aymenelghoul.pdf}, keyword = {Geometric prior, Forme, Higher-order actif contours, Champ de Phase, Directed networks} } Abstract : The segmentation of directed networks is an important problem in many domains, e.g. medical imaging (vascular networks) and remote sensing (river networks). Directed networks carry a unidirectional flow in each branch, which leads to characteristic geometric properties. In this paper, we present a nonlocal phase field model of directed networks. In addition to a scalar field representing a region by its smoothed characteristic function and interacting nonlocally so as to favour network configurations, the model contains a vector field representing the ‘flow’ through the network branches. The vector field is strongly encouraged to be zero outside, and of unit magnitude inside the region; and to have zero divergence. This prolongs network branches; controls width variation along a branch; and produces asymmetric junctions for which total incoming branch width approximately equals total outgoing branch width. In conjunction with a new interaction function, it also allows a broad range of stable branch widths. We analyse the energy to constrain the parameters, and show geometric experiments confirming the above behaviour. We also show a segmentation result on a synthetic river image.

7 - Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, août 2009. Mots-clés : Geometric prior, Forme, Contour actif d'ordre supérieur, Champ de Phase, remote sensing. Copyright : EURASIP @INPROCEEDINGS{ElGhoul09a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images}, year = {2009}, month = {août}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Glasgow, Scotland}, url = {http://hal.inria.fr/inria-00390446/fr/}, pdf = {http://hal.inria.fr/docs/00/39/04/46/PDF/eusipco09aymenelghoul.pdf}, keyword = {Geometric prior, Forme, Contour actif d'ordre supérieur, Champ de Phase, remote sensing} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. We incorporate such knowledge via phase fields and higher-order active contours (HOACs). In this paper: we introduce an improved prior model, the phase field HOAC ‘inflection point’ model of a network; we present an improved data term for the segmentation of road networks; we confirm the robustness of the resulting model to choice of gradient descent initialization; and we illustrate these points via road network extraction results on VHR satellite images.

8 - Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans International Conference on Pattern Recognition (ICPR), Tampa, Florida, décembre 2008. Mots-clés : Phase diagram, Higher-order actif contours, Forme, river extraction. @INPROCEEDINGS{ElGhoul08b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images}, year = {2008}, month = {décembre}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, Florida}, url = {https://hal.inria.fr/inria-00316619}, pdf = {http://hal.inria.fr/docs/00/31/66/19/PDF/icpr08aymenelghoul.pdf}, keyword = {Phase diagram, Higher-order actif contours, Forme, river extraction} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. Higher-order active contours provide a way to include such knowledge, but their behaviour can vary significantly with parameter values: e.g. the same energy can model networks or a ‘gas of circles’. In this paper, we present a stability analysis of a HOAC energy leading to the phase diagram of a long bar. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of network shapes using the energy. We apply the resulting model to the problem of hydrographic network extraction from VHR satellite images.

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