|
|
Publications of 2004
Result of the query in the list of publications :
10 Articles |
1 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 108--125, September 2004.
Note : in Russian
@ARTICLE{DES04br,
|
| author |
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{Descombes, X. and Zhizhina, E.}, |
| title |
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{Applications of Gibbs fields methods to image processing problems}, |
| year |
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{2004}, |
| month |
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{Problems of Information Transmission}, |
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| pages |
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{108--125}, |
| note |
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{in Russian}, |
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{} |
| } |
|
2 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 279-295, September 2004.
Note : in English
@ARTICLE{DES04be,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
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{Applications of Gibbs fields methods to image processing problems}, |
| year |
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{2004}, |
| month |
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{September}, |
| journal |
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{Problems of Information Transmission}, |
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| number |
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{3}, |
| pages |
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{279-295}, |
| note |
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{in English}, |
| url |
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{http://link.springer.com/article/10.1023%2FB%3APRIT.0000044262.70555.5c}, |
| keyword |
= |
{} |
| } |
|
3 - Modelling SAR Images with a Generalization of the Rayleigh Distribution.
E.E. Kuruoglu and J. Zerubia. IEEE Trans. Image Processing, 13(4): pages 527 - 533, April 2004.
@ARTICLE{Kuruoglu03,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling SAR Images with a Generalization of the Rayleigh Distribution}, |
| year |
= |
{2004}, |
| month |
= |
{April}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pages |
= |
{527 - 533}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284389.pdf?tp=&arnumber=1284389&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
4 - An object based approach for detecting smallbrain lesions: application to Virchow-Robin spaces.
X. Descombes and F. Kruggel and G. Wollny and H.J. Gertz. IEEE Trans. Medical Imaging, 23(2): pages 246--255, February 2004.
@ARTICLE{DES04a,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Wollny, G. and Gertz, H.J.}, |
| title |
= |
{An object based approach for detecting smallbrain lesions: application to Virchow-Robin spaces}, |
| year |
= |
{2004}, |
| month |
= |
{February}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{23}, |
| number |
= |
{2}, |
| pages |
= |
{246--255}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/42/28264/01263613.pdf?tp=&arnumber=1263613&isnumber=28264}, |
| keyword |
= |
{} |
| } |
|
5 - A Gibbs point process for road extraction in remotely sensed images.
R. Stoica and X. Descombes and J. Zerubia. International Journal of Computer Vision, 57(2): pages 121--136, 2004.
@ARTICLE{STO04a,
|
| author |
= |
{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Gibbs point process for road extraction in remotely sensed images}, |
| year |
= |
{2004}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{57}, |
| number |
= |
{2}, |
| pages |
= |
{121--136}, |
| url |
= |
{http://www.springerlink.com/content/kr262t6084464n30/}, |
| keyword |
= |
{} |
| } |
|
6 - An adaptive Gaussian model for satellite image deblurring.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. IEEE Trans. Image Processing, 13(4), 2004.
@ARTICLE{JAL04a,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{An adaptive Gaussian model for satellite image deblurring}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284396.pdf?tp=&arnumber=1284396&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
7 - Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification.
G. Rellier and X. Descombes and F. Falzon and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 42(7): pages 1543--1551, 2004.
@ARTICLE{DES04c,
|
| author |
= |
{Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, |
| title |
= |
{Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{42}, |
| number |
= |
{7}, |
| pages |
= |
{1543--1551}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/29162/01315838.pdf?tp=&arnumber=1315838&isnumber=29162}, |
| keyword |
= |
{} |
| } |
|
8 - Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux.
M. Ortner and X. Descombes and J. Zerubia. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 173-174: pages 83--92, 2004.
@ARTICLE{ORT04a,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux}, |
| year |
= |
{2004}, |
| journal |
= |
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, |
| volume |
= |
{173-174}, |
| pages |
= |
{83--92}, |
| keyword |
= |
{} |
| } |
|
9 - Gamma-convergence of discrete functionals with nonconvex perturbation for image classification.
G. Aubert and L. Blanc-Féraud and R. March. SIAM Journal on Numerical Analysis, 42(3): pages 1128--1145, 2004.
@ARTICLE{BLA04,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{Gamma-convergence of discrete functionals with nonconvex perturbation for image classification}, |
| year |
= |
{2004}, |
| journal |
= |
{SIAM Journal on Numerical Analysis}, |
| volume |
= |
{42}, |
| number |
= |
{3}, |
| pages |
= |
{1128--1145}, |
| url |
= |
{http://epubs.siam.org/doi/abs/10.1137/S0036142902412336}, |
| keyword |
= |
{} |
| } |
|
10 - A nonlinear entropic variational model for image filtering.
A. Ben Hamza and H. Krim and J. Zerubia. EURASIP Journal on Applied Signal Processing, 16: pages 2408--2422, 2004.
@ARTICLE{JZHK04,
|
| author |
= |
{Ben Hamza, A. and Krim, H. and Zerubia, J.}, |
| title |
= |
{A nonlinear entropic variational model for image filtering}, |
| year |
= |
{2004}, |
| journal |
= |
{EURASIP Journal on Applied Signal Processing}, |
| volume |
= |
{16}, |
| pages |
= |
{2408--2422}, |
| url |
= |
{https://hal.inria.fr/hal-00784485/}, |
| keyword |
= |
{} |
| } |
|
 top of the page
4 PhD Thesis and Habilitations |
1 - Processus Ponctuels Marqués pour l'Extraction Automatique de Caricatures de Bâtiments à partir de Modèles Numériques d'Elévation.
M. Ortner. PhD Thesis, Universite de Nice Sophia Antipolis, October 2004.
Keywords : Marked point process, Object extraction, Buildings, Digital Elevation Model (DEM), RJMCMC, Stochastic geometry.
@PHDTHESIS{mortner_these,
|
| author |
= |
{Ortner, M.}, |
| title |
= |
{Processus Ponctuels Marqués pour l'Extraction Automatique de Caricatures de Bâtiments à partir de Modèles Numériques d'Elévation}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00189803}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/18/98/03/PDF/These_Ortner.pdf}, |
| keyword |
= |
{Marked point process, Object extraction, Buildings, Digital Elevation Model (DEM), RJMCMC, Stochastic geometry} |
| } |
Résumé :
Cette thèse se place dans un cadre de reconstruction urbaine et propose un corpus algorithmique pour extraire des formes simples sur les Modèles Numériques d'Elévation. Ce type de données décrit le relief d'une zone urbaine par une grille régulière de points à chacun desquels est associée une information de hauteur.
Les modèles utilisés reposent sur l'utilisation de processus ponctuels marqués. Il s'agit de variables aléatoires dont les réalisations sont des configurations d'objets géométriques. Ces modèles permettent d'introduire des contraintes sur la forme des objets recherchés dans une image ainsi qu'un terme de régularisation modélisé par des interactions entre les objets. Une énergie peut être associée aux configurations d'objets et la configuration minimisant cette énergie trouvée au moyen d'un recuit-simulé couplé à un échantillonneur de type Monte Carlo par Chaîne de Markov à sauts réversibles (RJMCMC).
Nous proposons quatre modèles pour extraire des caricatures de bâtiments à partir de descriptions altimétriques de zones urbaines denses. Chaque modèle est constitué par une forme d'objet, une énergie d'attache aux données et une énergie de régularisation. Les deux premiers modèles permettent d'extraire des formes simples (rectangles) en utilisant une contrainte d'homogénéité pour l'un et une détection des discontinuités pour l'autre. Le troisième modèle modélise les bâtiments par une forme polyhédrique. Le dernier modèle s'intéresse à l'apport d'une coopération entre des objets simples. Les algorithmes obtenus, automatiques, sont évalués sur des données réelles fournies par l'IGN (MNE Laser et optiques de différentes qualités). |
Abstract :
The context of this thesis is the reconstruction of urban areas from images. It proposes a set of algorithms for extracting simple shapes from Digital Elevation Models (DEM). DEMs describe the altimetry of an urban area by a grid of points, each of which has a height associated to it.
The proposed models are based on marked point processes. These mathematical objects are random variables whose realizations are configurations of geometrical shapes. Using these processes, we can introduce constraints on the shape of the objects to be detected in an image, and a regularizing term incorporating geometrical interactions between objects. An energy can be associated to each object configuration, and the global minima of this energy can then be found by applying simulated annealing to a Reversible Jump Monte Carlo Markov Chain sampler (RJMCMC).
We propose four different models for extracting the outlines of buildings from altimetric descriptions of dense urban areas. Each of these models is constructed from an object shape, a data energy, and a regularizing energy.
The first two models extract simple shapes (rectangles) using, respectively, a homogeneity constraint and discontinuity detection. The third model looks for three-dimensional polyhedral buildings. The last model uses cooperation between two types of objects, rectangles and segments.
The resulting algorithms are evaluated on real data provided by the French National Geographic Institute (a laser DEM and optical DEMs of differing quality). |
|
2 - Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués.
C. Lacoste. PhD Thesis, Universite de Nice Sophia Antipolis, September 2004.
Keywords : Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process.
@PHDTHESIS{lacoste_these,
|
| author |
= |
{Lacoste, C.}, |
| title |
= |
{Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00261397}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/26/13/97/PDF/THESE_CAROLINE_LACOSTE.pdf}, |
| keyword |
= |
{Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process} |
| } |
Résumé :
Cette thèse aborde le problème de l'extraction non supervisée des réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires et aériennes. Nous utilisons des processus objet, ou processus ponctuels marqués, comme modèles a priori. Ces modèles permettent de bénéficier de l'apport d'un cadre stochastique (robustesse au bruit, corpus algorithmique, etc.) tout en manipulant des contraintes géométriques fortes. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation.
Nous proposons tout d'abord une modélisation du réseau linéique par un processus dont les objets sont des segments interagissant entre eux. Le modèle a priori est construit de façon à exploiter au mieux la topologie du réseau recherché au travers de potentiels fondés sur la qualité de chaque interaction. Les propriétés radiométriques sont prises en compte dans un terme d'attache aux données fondé sur des mesures statistiques.
Nous étendons ensuite cette modélisation à des objets plus complexes. La manipulation de lignes brisées permet une extraction plus précise du réseau et améliore la détection des bifurcations.
Enfin, nous proposons une modélisation hiérarchique des réseaux hydrographiques dans laquelle les affluents d'un fleuve sont modélisés par un processus de lignes brisées dans le voisinage de ce fleuve.
Pour chacun des modèles, nous accélérons la convergence de l'algorithme MCMC par l'ajout de perturbations adaptées.
La pertinence de cette modélisation par processus objet est vérifiée sur des images satellitaires et aériennes, optiques et radar. |
Abstract :
This thesis addresses the problem of the unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. We use object processes, or marked point processes, as prior models. These models benefit from a stochastic framework (robustness w.r.t. noise, algorithms, etc.) while incorporating strong geometric constraints. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization.
We first propose to model line networks by a process whose objects are interacting line segments. The prior model is designed to exploit as fully as possible the topological properties of the network under consideration through potentials based on the quality of each interaction. The radiometric properties of the network are modeled using a data term based on statistical measures.
We then extend this model to more complex objects. The use of broken lines improves the detection of network junctions and increases the accuracy of the extracted network.
Finally, we propose a hierarchical model of hydrographic networks in which the tributaries of a given river are modeled by a process of broken lines in the neighborhood of this river. For each model, we accelerate convergence of the RJMCMC algorithm by using appropriate perturbations.
We show experimental results on aerial and satellite images (optical and radar data) to verify the relevance of the object process models. |
|
3 - Contribution à l'Analyse de Textures en Traitement d'Images par Méthodes Variationnelles et Equations aux Dérivées Partielles.
J.F. Aujol. PhD Thesis, Universite de Nice Sophia Antipolis, June 2004.
Keywords : Image decomposition, Classification, Restoration, Fonctional analysis, Bounded Variation Space, Sobolev space.
@PHDTHESIS{JFAujol,
|
| author |
= |
{Aujol, J.F.}, |
| title |
= |
{Contribution à l'Analyse de Textures en Traitement d'Images par Méthodes Variationnelles et Equations aux Dérivées Partielles}, |
| year |
= |
{2004}, |
| month |
= |
{June}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00006303}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/04/68/89/PDF/tel-00006303.pdf}, |
| keyword |
= |
{Image decomposition, Classification, Restoration, Fonctional analysis, Bounded Variation Space, Sobolev space} |
| } |
Résumé :
Cette thèse est un travail en mathématiques appliquées. Elle aborde quelques problèmes en analyse d'images et utilise des outils mathématiques spécifiques.
L'objectif des deux premières parties de cette thèse est de proposer un modèle pour décomposer une image f'en trois composantes : f=u+v+w. Notre approche repose sur l'utilisation d'espaces mathématiques adaptés à chaque composante: l'espace BV des fonctions à variations bornées pour u, un espace G'proche du dual de BV pour les textures, et un espace de Besov d'exposant négatif E'pour le bruit. Nous effectuons l'étude mathématique complète des différents modèles que nous proposons. Nous illustrons notre approche par de nombreux exemples.Dans la troisième et dernière partie de cette thèse, nous nous intéressons spécifiquement à la composante texturée. Nous proposons un algorithme de classification supervisée pour les images texturées. |
Abstract :
This Ph.D. thesis is a work in applied mathematics. It deals with image processing problems, and uses specific mathematical tools.
The aim of the two first parts is to propose a model for decomposing an image f'into three components : f=u+v+w. Our approach relies on the use of mathematical spaces adapted to each component : the space BV of functions with bounded variations for u, a space G'close to the dual space of BV for v, and a negative Besov space E'for w. We carry out the complete mathematical analysis of the different models we propose. We illustrate our approach with many numerical examples. In the third and last part, we only deal with the texture component of an image. We propose a supervised classification algorithm for textured images. |
|
4 - Méthodes stochastiques en analyse d'image : des champs de Markov aux processus ponctuels marqués.
X. Descombes. Habilitation à diriger des Recherches, Universite de Nice Sophia Antipolis, February 2004.
Keywords : Markov Fields, Stochastic geometry.
@PHDTHESIS{Xdescombes,
|
| author |
= |
{Descombes, X.}, |
| title |
= |
{Méthodes stochastiques en analyse d'image : des champs de Markov aux processus ponctuels marqués}, |
| year |
= |
{2004}, |
| month |
= |
{February}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| type |
= |
{Habilitation à diriger des Recherches}, |
| url |
= |
{https://hal.inria.fr/tel-00506084}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/HDRdescombes.pdf}, |
| keyword |
= |
{Markov Fields, Stochastic geometry} |
| } |
|
top of the page
18 Conference articles |
1 - 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. |
|
2 - 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 |
= |
{} |
| } |
|
3 - 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. |
|
4 - Texture analysis using adaptative biorthogonal wavelet packets.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics.
@INPROCEEDINGS{Abhayratne_icip04,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using adaptative biorthogonal wavelet packets}, |
| 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/Abhayaratne04icip.pdf}, |
| keyword |
= |
{Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics} |
| } |
Abstract :
We discuss the use of adaptive biorthogonal wavelet packet bases
in a probabilistic approach to texture analysis, thus combining the
advantages of biorthogonal wavelets (FIR, linear phase) with those
of a coherent texture model. The computation of the probability
uses both the primal and dual coefcients of the adapted biorthogonal
wavelet packet basis. The computation of the biorthogonal
wavelet packet coefcients is done using a lifting scheme, which
is very efficient. The model is applied to the classification of mosaics
of Brodatz textures, the results showing improvement over
the performance of the corresponding orthogonal wavelets. |
|
5 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
@INPROCEEDINGS{lacoste04b,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Unsupervised line network extraction from remotely sensed images by polyline process}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7079995}, |
| pdf |
= |
{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1608.pdf}, |
| keyword |
= |
{} |
| } |
|
6 - A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry.
M. Ortner and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
Note : this paper has received a Young Authors award
@INPROCEEDINGS{ortner04b,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| note |
= |
{this paper has received a Young Authors award}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7079720}, |
| keyword |
= |
{} |
| } |
|
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