Publications of Josiane Zerubia

Result of the query in the list of publications :

• 59 Articles
• 173 Conference articles
• 64 Technical and Research Reports
• 7 Collection articles or Books chapters
• 3 Books

new search | get the bibtex

navigation : 1 - 2- ... - 7 - 8 - 9- ... - 15 - 16 display : 10 per page | 20 per page | 50 per page

173 Conference articles

82 - 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)

83 - 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 = {} }

84 - 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).

85 - 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} }

86 - 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} }

87 - 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.

88 - 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.

89 - Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares. F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu-Marni. In Proc. GRETSI Symposium on Signal and Image Processing, Louvain-la-Neuve, Belgique, September 2005. Keywords : Rare event, Forest fires, Gaussian Field. @INPROCEEDINGS{lafarge_gretsi05, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu-Marni, S.}, title = {Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares}, year = {2005}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Louvain-la-Neuve, Belgique}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_gretsi05.pdf}, keyword = {Rare event, Forest fires, Gaussian Field} }

90 - New Higher-order Active Contour Energies for Network Extraction. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Gap closure, Shape, Prior, Higher-order, Active contour. @INPROCEEDINGS{rochery_icip05, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {New Higher-order Active Contour Energies for Network Extraction}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip05.pdf}, keyword = {Gap closure, Shape, Prior, Higher-order, Active contour} } Abstract : Using the framework of higher-order active contours, we present a new quadratic em continuation energy for the extraction of line networks (e.g. road, hydrographic, vascular) in the presence of occlusions. Occlusions create gaps in the data that frequently translate to gaps in the extracted network. The new energy penalizes earby opposing extremities of the network, and thus favours the closure of the gaps created by occlusions. Nearby opposing extremities are identified using a sophisticated interaction between pairs of points on the contour. This new model allows the extraction of fully connected networks, even though occlusions violate common assumptions about the homogeneity of the interior, and high contrast with the exterior, of the network. We present experimental results on real aerial images that demonstrate the effectiveness of the new model for network extraction tasks.

91 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005. @INPROCEEDINGS{lacoste_eusipco05, author = {Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, title = {Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework}, year = {2005}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Antalya, Turkey}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, keyword = {} }

92 - Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction. F. Lafarge and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas. Copyright : IEEE @INPROCEEDINGS{lafarge_icip05, author = {Lafarge, F. and Descombes, X. and Zerubia, J.}, title = {Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_icip05.pdf}, keyword = {Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas} }

93 - Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model. A. Achim and E.E. Kuruoglu and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005. @INPROCEEDINGS{achim_eusipco_05, author = {Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, title = {Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model}, year = {2005}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Antalya, Turkey}, pdf = {http://kilyos.ee.bilkent.edu.tr/~signal/defevent/papers/cr1741.pdf}, keyword = {} }

94 - Texture-adaptive mother wavelet selection for texture analysis. G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Texture, Wavelet packet, Adaptive, Mother. @INPROCEEDINGS{abhayaratne_icip05, author = {Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, title = {Texture-adaptive mother wavelet selection for texture analysis}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, keyword = {Texture, Wavelet packet, Adaptive, Mother} } Abstract : Classification results obtained using wavelet-based texture analysis techniques vary with the choice of mother wavelet used in the methodology. We discuss the use of mother wavelet filters as parameters in a probabilistic approach to texture analysis based on adaptive biorthogonal wavelet packet bases. The optimal choice for the mother wavelet filters is estimated from the data, in addition to the other model parameters. The model is applied to the classification of single texture images and mosaics of Brodatz textures, the results showing improvement over the performance of standard wavelets for a given filter length.

95 - A Marked Point Process Model for Tree Crown Extraction in Plantations. G. Perrin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process. @INPROCEEDINGS{perrin_icip05, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {A Marked Point Process Model for Tree Crown Extraction in Plantations}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.ps.gz}, keyword = {Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process} } Abstract : This work presents a framework to extract tree crowns from remotely sensed data, especially in plantation images, using stochastic geometry. We aim at finding the tree top positions, and the tree crown diameter distribution. Our approach consists in considering that these images are some realizations of a marked point process. First we model the tree plantation as a configuration of an unknown number of ellipses. Then, a Bayesian energy is defined, containing both a prior energy which incorporates the prior knowledge of the plantation geometric properties, and a likelihood which fits the objects to the data. Eventually, we estimate the global minimum of this energy using Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing scheme. We present results on optical aerial images of poplars provided by IFN.

96 - Shape Moments for Region-Based Active Contours. P. Horvath and A. Bhattacharya and I. H. Jermyn and J. Zerubia and Z. Kato. In Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition, Szeged, Hungary, May 2005. @INPROCEEDINGS{horvath_hacippr05, author = {Horvath, P. and Bhattacharya, A. and Jermyn, I. H. and Zerubia, J. and Kato, Z.}, title = {Shape Moments for Region-Based Active Contours}, year = {2005}, month = {May}, booktitle = {Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition}, address = {Szeged, Hungary}, url = {http://vision.vein.hu/HACIPPR/}, keyword = {} }

97 - A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform. G. Pons Bernad and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Pennsylvania, USA, March 2005. @INPROCEEDINGS{pons_icassp2005, author = {Pons Bernad, G. and Blanc-Féraud, L. and Zerubia, J.}, title = {A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform}, year = {2005}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Philadelphia, Pennsylvania, USA}, pdf = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1415481}, keyword = {} } Abstract : Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution and/or denoising methods have been proposed to reduce these degradations.Here we propose a wavelet denoising method, which turns out to be very effective for three-dimensional confocal images. To obtain a translation and rotation invariant algorithm, we have developped the 3D Complex Wavelet Transform introduced by N. Kingsbury. These wavelets allow moreover a better directional selectivity of the wavelet coefficients. We show on simulated and real biological data the good performances of this algorithm.

98 - Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory. R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. Physics in Signal and Image Processing, Toulouse, France, January 2005. Keywords : Bimodal, Statistics, Wavelet packet, Adaptive, Texture. @INPROCEEDINGS{cossu_psip05, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory}, year = {2005}, month = {January}, booktitle = {Proc. Physics in Signal and Image Processing}, address = {Toulouse, France}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, keyword = {Bimodal, Statistics, Wavelet packet, Adaptive, Texture} } Abstract : In recent work, it was noted that although the subband histograms for standard wavelet coefcients take on a generalized Gaussian form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, generalized Gaussian, and interestingly, in some subbands, bi- or multi-modal histograms. Motivated by this observation, we provide additional experimental conrmation of the existence of multimodal subbands, and provide a theoretical explanation for their occurrence. The results reveal the connection of such subbands with the characteristic structure in a texture, and thus confirm the importance of such subbands for image modelling and applications.

99 - Texture discrimination using multimodal wavelet packet subbands. R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004. Keywords : Bimodal, Adaptive, probabilistic, Wavelet packet, Texture. @INPROCEEDINGS{cossu_icip04, author = {Cossu, R. and Jermyn, I. H. and Zerubia, J.}, title = {Texture discrimination using multimodal wavelet packet subbands}, year = {2004}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Singapore}, pdf = {http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icip.pdf}, keyword = {Bimodal, Adaptive, probabilistic, Wavelet packet, Texture} } Abstract : The subband histograms of wavelet packet bases adapted to individual texture classes often fail to display the leptokurtotic behaviour shown by the standard wavelet coefcients of `natural' images. While many subband histograms remain leptokurtotic in adaptive bases, some subbands are Gaussian. Most interestingly, however, some subbands show multimodal behaviour, with no mode at zero. In this paper, we provide evidence for the existence of these multimodal subbands and show that they correspond to narrow frequency bands running throughout images of the texture. They are thus closely linked to the texture's structure. As such, they seem likely to possess superior descriptive and discriminative power as compared to unimodal subbands. We demonstrate this using both Brodatz and remote sensing images.

100 - Segmentation of remote sensing images by supervised TS-MRF. G. Poggi and G. Scarpa and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004. @INPROCEEDINGS{poggi_icip04, author = {Poggi, G. and Scarpa, G. and Zerubia, J.}, title = {Segmentation of remote sensing images by supervised TS-MRF}, year = {2004}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Singapore}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1421441}, keyword = {} }

101 - Gap closure in (road) networks using higher-order active contours. M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004. Keywords : Active contour, Gap closure, Higher-order, Shape, Road network. @INPROCEEDINGS{Rochery04, author = {Rochery, M. and Jermyn, I. H. and Zerubia, J.}, title = {Gap closure in (road) networks using higher-order active contours}, year = {2004}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Singapore}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip04.pdf}, keyword = {Active contour, Gap closure, Higher-order, Shape, Road network} } Abstract : We present a new model for the extraction of networks from images in the presence of occlusions. Such occlusions cause gaps in the extracted network that need to be closed. Using higher-order active contours, which allow the incorporation of sophisticated geometric information, we introduce a new, non-local, `gap closure' force that causes pairs of network extremities that are close together to extend towards one another and join, thus closing the gap between them. We demonstrate the benefits of the model using the problem of road network extraction, presenting results on aerial images.

top of the page

navigation : 1 - 2- ... - 7 - 8 - 9- ... - 15 - 16 display : 10 per page | 20 per page | 50 per page

These pages were generated by