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Publications de type 'inproceedings'
Résultat de la recherche dans la liste des publications :
245 Articles de conférence |
41 - High resolution SAR-image classification by Markov random fields and finite mixtures.
G. Moser et V. Krylov et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010), Vol. 7533, pages 753308, San Jose, USA, janvier 2010.
Mots-clés : SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula.
Copyright : SPIE
@INPROCEEDINGS{moserSPIE2010a,
|
| author |
= |
{Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{High resolution SAR-image classification by Markov random fields and finite mixtures}, |
| year |
= |
{2010}, |
| month |
= |
{janvier}, |
| booktitle |
= |
{Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010)}, |
| volume |
= |
{7533}, |
| pages |
= |
{753308}, |
| address |
= |
{San Jose, USA}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=776565}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00442348/en/}, |
| keyword |
= |
{SAR image classification, Dictionnaire, amplitude probability density, EM Stochastique (SEM), Markov random field, copula} |
| } |
Abstract :
In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for SAR amplitude probability density function estimation. For modeling the joint distribution from marginals corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic supervised algorithm is validated in the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed. |
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42 - 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. |
|
43 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics.
C. Benedek et X. Descombes et J. Zerubia. Dans IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, décembre 2009.
Mots-clés : Processus ponctuels marques, Change detection, Aerial images, Building extraction, Imagerie satellitaire.
@INPROCEEDINGS{benedekWacv09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, |
| year |
= |
{2009}, |
| month |
= |
{décembre}, |
| booktitle |
= |
{IEEE Workshop on Applications of Computer Vision (WACV)}, |
| pages |
= |
{100-105}, |
| address |
= |
{Snowbird, Utah, USA}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, |
| keyword |
= |
{Processus ponctuels marques, Change detection, Aerial images, Building extraction, Imagerie satellitaire} |
| } |
Abstract :
In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features.
|
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44 - Reconstruction 3D du bâti par la technique des ombres chinoises.
P. Lukashevish et A. Kraushonak et X. Descombes et J.D. Durou et B. Zalessky et E. Zhizhina. Dans GRETSI Dijon, Dijon, France, novembre 2009.
Mots-clés : Reconstruction en 3D.
@INPROCEEDINGS{luka09,
|
| author |
= |
{Lukashevish, P. and Kraushonak, A. and Descombes, X. and Durou, J.D. and Zalessky, B. and Zhizhina, E.}, |
| title |
= |
{Reconstruction 3D du bâti par la technique des ombres chinoises}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{GRETSI Dijon}, |
| address |
= |
{Dijon, France}, |
| url |
= |
{http://hal.inria.fr/inria-00399208/fr/}, |
| keyword |
= |
{Reconstruction en 3D} |
| } |
|
45 - Combining meshes and geometric primitives for accurate and semantic modeling.
F. Lafarge et R. Keriven et M. Brédif. Dans Proc. British Machine Vision Conference (BMVC), London, U.K., novembre 2009.
@INPROCEEDINGS{lafarge_bmvc09,
|
| author |
= |
{Lafarge, F. and Keriven, R. and Brédif, M.}, |
| title |
= |
{Combining meshes and geometric primitives for accurate and semantic modeling}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{London, U.K.}, |
| url |
= |
{http://recherche.ign.fr/labos/matis/pdf/articles_conf/2009/bmvc_final_09.pdf}, |
| keyword |
= |
{} |
| } |
|
46 - 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} |
| } |
|
47 - Object extraction from high resolution SAR images using a birth and death dynamics.
F. Arslan et X. Descombes et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, novembre 2009.
Mots-clés : High resolution SAR images, Extraction d'objets, Processus ponctuels marques, birth and death process.
@INPROCEEDINGS{Fatih09,
|
| author |
= |
{Arslan, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Object extraction from high resolution SAR images using a birth and death dynamics}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Cairo, Egypt}, |
| url |
= |
{http://dx.doi.org/10.1109/ICIP.2009.5413907}, |
| keyword |
= |
{High resolution SAR images, Extraction d'objets, Processus ponctuels marques, birth and death process} |
| } |
Abstract :
We present a new approach to extract predefined objects, such as trees and oil tanks for instance, from high resolution SAR images. We consider a stochastic approach based on an object process also called marked point process. The objects represent trees or oil tanks which are modeled by disks in the image. We first define a Gibbs density that takes into account both prior information and the data. The energy we define is composed of two terms, one is a prior, penalizing overlaps between objects, and the other is a data term, which measures the suitability of an object in the SAR image. The problem is then reduced to an energy minimization problem. We sample the process to extract the configuration of objects minimizing the energy by a fast birth-and-death dynamics, leading to the total number of objects (trees or oil tanks in our case). This approach is much faster than manual counts and does not need any preprocessing or supervision of a user. |
|
48 - Multi-class SVM for forestry classification.
N. Hajj Chehade et JG. Boureau et C. Vidal et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, novembre 2009.
Mots-clés : Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation.
@INPROCEEDINGS{Nabil09,
|
| author |
= |
{Hajj Chehade, N. and Boureau, JG. and Vidal, C. and Zerubia, J.}, |
| title |
= |
{Multi-class SVM for forestry classification}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Cairo, Egypt}, |
| url |
= |
{http://dx.doi.org/10.1109/ICIP.2009.5413395}, |
| keyword |
= |
{Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation} |
| } |
Abstract :
In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM), which is a popular supervised learning technique for classification. The choice of features (along with their corresponding parameters), the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done on several CIR aerial images provided by the French National Forest Inventory (IFN). In this paper, we will show one example on a national forest near Sedan (in France), and compare our result with the IFN map. |
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49 - Estimation des paramètres de processus ponctuels marqués dans le cadre de l'extraction d’objets en imagerie de télédétection.
F. Chatelain et X. Descombes et J. Zerubia. Dans Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, novembre 2009.
@INPROCEEDINGS{cha09a,
|
| author |
= |
{Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Estimation des paramètres de processus ponctuels marqués dans le cadre de l'extraction d’objets en imagerie de télédétection}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. Symposium on Signal and Image Processing (GRETSI)}, |
| address |
= |
{Dijon, France}, |
| url |
= |
{http://hal.inria.fr/inria-00399258/fr/}, |
| keyword |
= |
{} |
| } |
|
50 - Lidar Waveform Modeling using a Marked Point Process.
C. Mallet et F. Lafarge et F. Bretar et U. Soergel et C. Heipke. Dans Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, novembre 2009.
Mots-clés : 3D point cloud, Lidar, Marked point process, RJMCMC.
@INPROCEEDINGS{mallet_icip09,
|
| author |
= |
{Mallet, C. and Lafarge, F. and Bretar, F. and Soergel, U. and Heipke, C.}, |
| title |
= |
{Lidar Waveform Modeling using a Marked Point Process}, |
| year |
= |
{2009}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Cairo, Egypt}, |
| url |
= |
{http://dx.doi.org/10.1109/ICIP.2009.5413380}, |
| keyword |
= |
{3D point cloud, Lidar, Marked point process, RJMCMC} |
| } |
Abstract :
Lidar waveforms are 1D signal consisting of a train of echoes where each of them correspond to a scattering target of the Earth surface. Modeling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. This paper presents a marked point process based model to reconstruct a lidar signal in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the models and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a simulated annealing. Results are finally presented on different kinds of signals in urban areas. |
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