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Publications of Xavier Descombes
Result of the query in the list of publications :
44 Articles |
11 - Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations.
S. Descamps and X. Descombes and A. Béchet and J. Zerubia. Traitement du Signal, 26(2): pages 95-108, July 2009.
Keywords : flamants roses.
@ARTICLE{flamTS,
|
| author |
= |
{Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, |
| title |
= |
{Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations}, |
| year |
= |
{2009}, |
| month |
= |
{July}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{26}, |
| number |
= |
{2}, |
| pages |
= |
{95-108}, |
| pdf |
= |
{http://documents.irevues.inist.fr/handle/2042/28809}, |
| keyword |
= |
{flamants roses} |
| } |
Résumé :
Nous présentons dans cet article une nouvelle technique de détection de flamants roses sur des images aériennes. Nous considérons une approche stochastique fondée sur les processus ponctuels marqués, aussi appelés processus objets. Ici, les objets représentent les flamants, qui sont modélisés par des ellipses. La densité associée au processus ponctuel marqué d'ellipses est définie par rapport à une mesure de Poisson. Dans un cadre gibbsien, le problème se réduit à la minimisation d'une énergie, qui est constituée d'un terme de régularisation (densité a priori), qui introduit des contraintes sur les objets et leurs interactions; et un terme d'attache aux données, qui permet de localiser sur l'image les flamants à extraire. Nous échantillonnons le processus pour extraire la configuration d'objets minimisant l'énergie grâce à une nouvelle dynamique de Naissances et Morts multiples, amenant finalement à une estimation du nombre total de flamants présents sur l'image. Cette approche donne des comptes avec une bonne précision comparée aux comptes manuels. De plus, elle ne nécessite aucun traitement préalable ou intervention manuelle, ce qui réduit considérablement le temps d'obtention des comptes. |
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12 - Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum.
X. Descombes and R. Minlos and E. Zhizhina. Journal of Mathematical Imaging and Vision, 33(3): pages 347-359, 2009.
Keywords : birth and death process, Marked point process, Object extraction.
Copyright : Springer
@ARTICLE{DZM08,
|
| author |
= |
{Descombes, X. and Minlos, R. and Zhizhina, E.}, |
| title |
= |
{Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum}, |
| year |
= |
{2009}, |
| journal |
= |
{Journal of Mathematical Imaging and Vision}, |
| volume |
= |
{33}, |
| number |
= |
{3}, |
| pages |
= |
{347-359}, |
| pdf |
= |
{http://dx.doi.org/10.1007/s10851-008-0117-y}, |
| keyword |
= |
{birth and death process, Marked point process, Object extraction} |
| } |
Abstract :
We define a new birth and death dynamics dealing with configurations of disks in the plane. We prove the convergence of the continuous process and propose a discrete scheme converging to the continuous case. This framework is developed to address image processing problems consisting in detecting a configuration of objects from a digital image. The derived algorithm is applied for tree crown extraction and bird detection from aerial images. The performance of this approach is shown on real data. |
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13 - Automatic Building Extraction from DEMs using an Object Approach and Application to the 3D-city Modeling.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Journal of Photogrammetry and Remote Sensing, 63(3): pages 365-381, May 2008.
Keywords : Building extraction, 3D reconstruction, Digital Elevation Model, Stochastic geometry.
@ARTICLE{lafarge_jprs08,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic Building Extraction from DEMs using an Object Approach and Application to the 3D-city Modeling}, |
| year |
= |
{2008}, |
| month |
= |
{May}, |
| journal |
= |
{Journal of Photogrammetry and Remote Sensing}, |
| volume |
= |
{63}, |
| number |
= |
{3}, |
| pages |
= |
{365-381}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2008_lafarge_jprs08.pdf}, |
| keyword |
= |
{Building extraction, 3D reconstruction, Digital Elevation Model, Stochastic geometry} |
| } |
Abstract :
In this paper, we present an automatic building extraction method from Digital Elevation Models based on an object approach.
First, a rough approximation of the building footprints is realized by a method based on marked point processes: the building
footprints are modeled by rectangle layouts. Then, these rectangular footprints are regularized by improving the connection
between the neighboring rectangles and detecting the roof height discontinuities. The obtained building footprints are structured
footprints: each element represents a specific part of an urban structure. Results are finally applied to a 3D-city modeling process. |
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14 - Gap Filling of 3-D Microvascular Networs by Tensor Voting.
L. Risser and F. Plouraboue and X. Descombes. IEEE Trans. Medical Imaging, 27(5): pages 674-687, May 2008.
Copyright :
@ARTICLE{xavTMI3,
|
| author |
= |
{Risser, L. and Plouraboue, F. and Descombes, X.}, |
| title |
= |
{Gap Filling of 3-D Microvascular Networs by Tensor Voting}, |
| year |
= |
{2008}, |
| month |
= |
{May}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{27}, |
| number |
= |
{5}, |
| pages |
= |
{674-687}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/42/4497376/04389807.pdf?isnumber=4497376&prod=JNL&arnumber=4389807&arSt=674&ared=687&arAuthor=Risser%2C+L.%3B+Plouraboue%2C+F.%3B+Descombes%2C+X.}, |
| keyword |
= |
{} |
| } |
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15 - A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models..
M. Ortner and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 2008.
Keywords : Image procressing, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register.
Copyright :
@ARTICLE{ortner08,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.}, |
| year |
= |
{2008}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/27/88/82/PDF/ortner08.pdf}, |
| keyword |
= |
{Image procressing, Poisson point process, Stochastic geometry, Dense urban area, Digital Elevation Model, land register} |
| } |
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16 - The Gibbs fields approach and related dynamics in image processing.
X. Descombes and E. Zhizhina. Condensed Matter Physics, 11(2(54)): pages 293-312, 2008.
Copyright : Institute for Condensed Matter
@ARTICLE{LNA08,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
= |
{The Gibbs fields approach and related dynamics in image processing}, |
| year |
= |
{2008}, |
| journal |
= |
{Condensed Matter Physics}, |
| volume |
= |
{11}, |
| number |
= |
{2(54)}, |
| pages |
= |
{293-312}, |
| keyword |
= |
{} |
| } |
|
17 - Building Outline Extraction from Digital Elevation Models using Marked Point Processes.
M. Ortner and X. Descombes and J. Zerubia. International Journal of Computer Vision, 72(2): pages 107-132, April 2007.
Keywords : RJMCMC, Buildings, Stochastic geometry, Marked point process, Digital Elevation Model (DEM).
@ARTICLE{ortner_ijcv_05,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Outline Extraction from Digital Elevation Models using Marked Point Processes}, |
| year |
= |
{2007}, |
| month |
= |
{April}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{72}, |
| number |
= |
{2}, |
| pages |
= |
{107-132}, |
| url |
= |
{http://www.springerlink.com/content/d563v16957427102/?p=873bd324c7c14049a45cc1f2905b5a86&pi=0}, |
| keyword |
= |
{RJMCMC, Buildings, Stochastic geometry, Marked point process, Digital Elevation Model (DEM)} |
| } |
|
18 - ant colony optimization for image regularization based on a non-stationary Markov modeling.
S. Le Hegarat-Mascle and A. Kallel and X. Descombes. IEEE Trans. on Image Processing, 16(3): pages 865-878, March 2007.
Keywords : Markov Random Fields, Ants colonization.
@ARTICLE{Ants07,
|
| author |
= |
{Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, |
| title |
= |
{ant colony optimization for image regularization based on a non-stationary Markov modeling}, |
| year |
= |
{2007}, |
| month |
= |
{March}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{16}, |
| number |
= |
{3}, |
| pages |
= |
{865-878}, |
| keyword |
= |
{Markov Random Fields, Ants colonization} |
| } |
Abstract :
Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixed-form neighborhood used in classical Markov random field regularization techniques. The performance of this new approach is illustrated on a simulated image and on actual remote sensing images |
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19 - Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques.
F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu. Traitement du Signal, 24(1), 2007.
Note : copyright Traitement du Signal
Keywords : Gaussian Field, Rare event, DT-caracteristic, Intensity peak.
@ARTICLE{lafarge_ts06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu, S.}, |
| title |
= |
{Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques}, |
| year |
= |
{2007}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{24}, |
| number |
= |
{1}, |
| note |
= |
{copyright Traitement du Signal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_ts06.pdf}, |
| keyword |
= |
{Gaussian Field, Rare event, DT-caracteristic, Intensity peak} |
| } |
|
20 - Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images.
A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and 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. |
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