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Publications of Florent Lafarge
Result of the query in the list of publications :
8 Articles |
1 - A Marked Point Process for Modeling Lidar Waveforms.
C. Mallet and F. Lafarge and M. Roux and U. Soergel and F. Bretar and C. Heipke. IEEE Trans. Image Processing, 19(12): pages 3204-3221, December 2010.
Keywords : Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model.
@ARTICLE{mallet_tip2010,
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{A Marked Point Process for Modeling Lidar Waveforms}, |
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{IEEE Trans. Image Processing}, |
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{3204-3221}, |
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{http://dx.doi.org/10.1109/TIP.2010.2052825}, |
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{Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model} |
| } |
Abstract :
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based on a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a Reversible Jump Markov Chain Monte Carlo sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported. |
|
2 - Geometric Feature Extraction by a Multi-Marked Point Process .
F. Lafarge and G. Gimel'farb and X. Descombes. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(9): pages 1597-1609, September 2010.
Keywords : Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing.
@ARTICLE{pami09b_lafarge,
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{Geometric Feature Extraction by a Multi-Marked Point Process }, |
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{2010}, |
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{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
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{1597-1609}, |
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{http://dx.doi.org/10.1109/TPAMI.2009.152}, |
| keyword |
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{Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing} |
| } |
Abstract :
This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency. |
|
3 - Insertion of 3D-primitives in mesh-based representations: Towards compact models preserving the details.
F. Lafarge and R. Keriven and M. Brédif. IEEE Trans. Image Processing, 19(7): pages 1683-1694, July 2010.
Keywords : 3-D reconstruction, Graph-cut , Shape extraction, urban scenes.
@ARTICLE{lafarge_tip2010,
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{Insertion of 3D-primitives in mesh-based representations: Towards compact models preserving the details}, |
| year |
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{2010}, |
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{IEEE Trans. Image Processing}, |
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{1683-1694}, |
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{http://dx.doi.org/10.1109/TIP.2010.2045695}, |
| keyword |
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{3-D reconstruction, Graph-cut , Shape extraction, urban scenes} |
| } |
Abstract :
We propose an original hybrid modeling process of urban scenes that represents 3-D models as a combination of mesh-based surfaces and geometric 3-D-primitives. Meshes describe details such as ornaments and statues, whereas 3-D-primitives code for regular shapes such as walls and columns. Starting from an 3-D-surface obtained by multiview stereo techniques, these primitives are inserted into the surface after being detected. This strategy allows the introduction of semantic knowledge, the simplification of the modeling, and even correction of errors generated by the acquisition process. We design a hierarchical approach exploring different scales of an observed scene. Each level consists first in segmenting the surface using a multilabel energy model optimized by -expansion and then in fitting 3-D-primitives such as planes, cylinders or tori on the obtained partition where relevant. Experiments on real meshes, depth maps and synthetic surfaces show good potential for the proposed approach. |
|
4 - Structural approach for building reconstruction from a single DSM.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(1): pages 135-147, January 2010.
@ARTICLE{lafarge_pami09,
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{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
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{Structural approach for building reconstruction from a single DSM}, |
| year |
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{2010}, |
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{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
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{32}, |
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{135-147}, |
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{http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.281}, |
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{} |
| } |
Abstract :
We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGO set). First, the 2D-supports of the urban structures are extracted either interactively or automatically. Then, 3D-blocks are placed on the 2D-supports using a Gibbs model which controls both the block assemblage and the fitting to data. A Bayesian decision finds the optimal configuration of 3D--blocks using a Markov Chain Monte Carlo sampler associated with original proposition kernels. This method has been validated on multiple data set in a wide-resolution interval such as 0.7 m satellite and 0.1 m aerial DSMs, and provides 3D representations on complex buildings and dense urban areas with various levels of detail. |
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5 - 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 |
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{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
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{Automatic Building Extraction from DEMs using an Object Approach and Application to the 3D-city Modeling}, |
| year |
= |
{2008}, |
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{May}, |
| journal |
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{Journal of Photogrammetry and Remote Sensing}, |
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{63}, |
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{3}, |
| pages |
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{365-381}, |
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| keyword |
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{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. |
|
6 - 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 |
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{Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu, S.}, |
| title |
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{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} |
| } |
|
7 - Automatic building 3D reconstruction from DEMs.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 184: pages 48--53, 2006.
Keywords : 3D-reconstruction, Digital Elevation Model, Building extraction, dense urban areas.
@ARTICLE{lafarge_sfpt06,
|
| author |
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{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic building 3D reconstruction from DEMs}, |
| year |
= |
{2006}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| volume |
= |
{184}, |
| pages |
= |
{48--53}, |
| url |
= |
{http://isprs.free.fr/documents/Papers/T07-32.pdf}, |
| keyword |
= |
{3D-reconstruction, Digital Elevation Model, Building extraction, dense urban areas} |
| } |
Abstract :
This paper is about an example of PLEIADES applications, the 3D building reconstruction. The future PLEIADES satellites are
especially well adapted to deal with 3D building reconstruction through the sub-metric resolution of images and its stereoscopic characteristics. We propose a fully automatic 3D-city model of dense urban areas using a parametric approach. First, a Digital Elevation
Model (DEM) is generated using an algorithm based on a maximum-flow formulation using three views. Then, building footprints are extracted from the DEM through an automatic method based on marked point processes : they are represented by an association of rectangles that we regularize by improving the connection of the neighboring rectangles and the facade discontinuity detection. Finally, a 3D-reconstruction method based on a skeleton process which allows to model the rooftops is proposed from the DEM and the building footprints. The different building heights constitute parameters which are estimated and then regularized by the ”K-means” algorithm including an entropy term. |
|
8 - Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 180: pages 4--12, 2005.
Keywords : 3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images.
Copyright : SFPT
@ARTICLE{lafarge_sfpt05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
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{Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution}, |
| year |
= |
{2005}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| volume |
= |
{180}, |
| pages |
= |
{4--12}, |
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= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_sfpt05.pdf}, |
| keyword |
= |
{3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images} |
| } |
|
 top of the page
PhD Thesis and Habilitation |
1 - Modèles stochastiques pour la reconstruction tridimensionnelle d'environnements urbains.
F. Lafarge. PhD Thesis, Ecole des Mines de Paris, October 2007.
Keywords : 3D reconstruction, Urban areas, Satellite images, Structural approach, Simulated Annealing, MCMC.
@PHDTHESIS{lafarge_phd07,
|
| author |
= |
{Lafarge, F.}, |
| title |
= |
{Modèles stochastiques pour la reconstruction tridimensionnelle d'environnements urbains}, |
| year |
= |
{2007}, |
| month |
= |
{October}, |
| school |
= |
{Ecole des Mines de Paris}, |
| url |
= |
{http://tel.archives-ouvertes.fr/tel-00179695/en/}, |
| keyword |
= |
{3D reconstruction, Urban areas, Satellite images, Structural approach, Simulated Annealing, MCMC} |
| } |
Résumé :
Cette thèse aborde le problème de la reconstruction tridimensionnelle de zones urbaines à partir d'images satellitaires très haute résolution. Le contenu informatif de ce type de données est insuffisant pour permettre une utilisation efficace des nombreux algorithmes développés pour des données aériennes. Dans ce contexte, l'introduction de connaissances a priori fortes sur les zones urbaines est nécessaire. Les outils stochastiques sont particulièrement bien adaptés pour traiter cette problématique.
Nous proposons une approche structurelle pour aborder ce sujet. Cela consiste à modéliser un bâtiment comme un assemblage de modules urbains élémentaires extraits d'une bibliothèque de modèles 3D paramétriques. Dans un premier temps, nous extrayons les supports 2D de ces modules à partir d'un Modèle Numérique d' Elévation (MNE). Le résultat est un agencement de quadrilatères dont les éléments voisins sont connectés entre eux. Ensuite, nous reconstruisons les bâtiments en recherchant la configuration optimale de modèles 3D se fixant sur les supports précédemment extraits. Cette configuration correspond à la réalisation qui maximise une densité mesurant la cohérence entre la réalisation et le MNE, mais également prenant en compte des connaissances a priori telles que des lois d'assemblage des modules. Nous discutons enfin de la pertinence de cette approche en analysant les résultats obtenus à partir de données satellitaires (simulations PLEIADES). Des expérimentations sont également réalisées à partir d'images aériennes mieux résolues. |
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top of the page
18 Conference articles |
1 - Building large urban environments from unstructured point data.
F. Lafarge and C. Mallet. In Proc. IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, November 2011.
@INPROCEEDINGS{lafarge_iccv11,
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{Lafarge, F. and Mallet, C.}, |
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{Building large urban environments from unstructured point data}, |
| year |
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{2011}, |
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{November}, |
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{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
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{Barcelona, Spain}, |
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{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126353}, |
| keyword |
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{} |
| } |
|
2 - Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar.
Y. Verdié and F. Lafarge and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011.
Keywords : 3D-Modeling, shape analysis, Mesh processing.
@INPROCEEDINGS{VerdieICIP11,
|
| author |
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{Verdié, Y. and Lafarge, F. and Zerubia, J.}, |
| title |
= |
{Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00605623/fr/}, |
| keyword |
= |
{3D-Modeling, shape analysis, Mesh processing} |
| } |
Abstract :
We present an automatic approach for modeling buildings from aerial LiDAR data. The method produces accurate, watertight and compact meshes under planar constraints which are especially designed for urban scenes. The LiDAR point cloud is classified through a non-convex energy minimization problem in order to separate the points labeled as building. Roof structures are then extracted from this point subset, and used to control the meshing procedure. Experiments highlight the potential of our method in term of minimal rendering, accuracy and compactness |
|
3 - Hybrid Multi-view Reconstruction by Jump-Diffusion.
F. Lafarge and R. Keriven and M. Brédif and H. Vu. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), San Franscico, U.S., June 2010.
@INPROCEEDINGS{lafarge_cvpr10,
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| author |
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{Lafarge, F. and Keriven, R. and Brédif, M. and Vu, H.}, |
| title |
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{Hybrid Multi-view Reconstruction by Jump-Diffusion}, |
| year |
= |
{2010}, |
| month |
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{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
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{San Franscico, U.S.}, |
| pdf |
= |
{http://certis.enpc.fr/publications/papers/CVPR10a.pdf}, |
| keyword |
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{} |
| } |
|
4 - Combining meshes and geometric primitives for accurate and semantic modeling.
F. Lafarge and R. Keriven and M. Brédif. In Proc. British Machine Vision Conference (BMVC), London, U.K., November 2009.
@INPROCEEDINGS{lafarge_bmvc09,
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| title |
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{Combining meshes and geometric primitives for accurate and semantic modeling}, |
| year |
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{2009}, |
| month |
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{Proc. British Machine Vision Conference (BMVC)}, |
| address |
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{http://recherche.ign.fr/labos/matis/pdf/articles_conf/2009/bmvc_final_09.pdf}, |
| keyword |
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{} |
| } |
|
5 - Lidar Waveform Modeling using a Marked Point Process.
C. Mallet and F. Lafarge and F. Bretar and U. Soergel and C. Heipke. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009.
Keywords : 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 |
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{Lidar Waveform Modeling using a Marked Point Process}, |
| year |
= |
{2009}, |
| month |
= |
{November}, |
| 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. |
|
6 - Texture representation by geometric objects using a jump-diffusion process.
F. Lafarge and G. Gimel'farb. In Proc. British Machine Vision Conference (BMVC), Leeds, U.K., November 2008.
@INPROCEEDINGS{lafarge_bmvc08,
|
| author |
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{Lafarge, F. and Gimel'farb, G.}, |
| title |
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{Texture representation by geometric objects using a jump-diffusion process}, |
| year |
= |
{2008}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Leeds, U.K.}, |
| url |
= |
{http://www.comp.leeds.ac.uk/bmvc2008/proceedings/papers/86.pdf}, |
| keyword |
= |
{} |
| } |
|
7 - A Geometric Primitive Extraction Process for Remote Sensing Problems..
F. Lafarge and G. Gimel'farb and X. Descombes. In Proc. Advanced Concepts for Intelligent Vision Systems, pages 518-529, Juan-les-Pins, France, October 2008.
Copyright :
@INPROCEEDINGS{LGF2008,
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| author |
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{Lafarge, F. and Gimel'farb, G. and Descombes, X.}, |
| title |
= |
{A Geometric Primitive Extraction Process for Remote Sensing Problems.}, |
| year |
= |
{2008}, |
| month |
= |
{October}, |
| booktitle |
= |
{ACIVS}, |
| pages |
= |
{518-529}, |
| address |
= |
{Juan-les-Pins, France}, |
| pdf |
= |
{http://www.springerlink.com/content/b228321527177226/}, |
| keyword |
= |
{} |
| } |
|
8 - A new computationally efficient stochastic approach for building reconstruction from satellite data.
F. Lafarge and M. Durupt and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In XXI ISPRS Congress, Part A, Beijing, China, July 2008.
Note : Copyright ISPRS
Keywords : 3D reconstruction, Building, satellite data, stochastic approach, jump process.
@INPROCEEDINGS{lafarge_isprs08,
|
| author |
= |
{Lafarge, F. and Durupt, M. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A new computationally efficient stochastic approach for building reconstruction from satellite data}, |
| year |
= |
{2008}, |
| month |
= |
{July}, |
| booktitle |
= |
{XXI ISPRS Congress, Part A}, |
| address |
= |
{Beijing, China}, |
| note |
= |
{Copyright ISPRS}, |
| url |
= |
{http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/40.pdf}, |
| keyword |
= |
{3D reconstruction, Building, satellite data, stochastic approach, jump process} |
| } |
|
9 - Building reconstruction from a single DEM.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, U.S., June 2008.
@INPROCEEDINGS{lafarge_cvpr08,
|
| author |
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{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Building reconstruction from a single DEM}, |
| year |
= |
{2008}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Anchorage, Alaska, U.S.}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2008_lafarge_cvpr08.pdf}, |
| keyword |
= |
{} |
| } |
|
10 - Automatic 3D modeling of urban scenes from satellite images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. SPACEAPPLI, Toulouse, France, April 2008.
@INPROCEEDINGS{lafarge_spaceappli08,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic 3D modeling of urban scenes from satellite images}, |
| year |
= |
{2008}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. SPACEAPPLI}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://www.toulousespaceshow.eu/tss08/spaceappli08/index.htm}, |
| keyword |
= |
{} |
| } |
|
11 - Forest Fire Detection based on Gaussian field analysis.
F. Lafarge and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007.
Note : Copyright EURASIP
Keywords : Gaussian Field, DT-caracteristic, Forest fires.
@INPROCEEDINGS{lafarge_eusipco07,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Forest Fire Detection based on Gaussian field analysis}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Poznan, Poland}, |
| note |
= |
{Copyright EURASIP}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_eusipco07.pdf}, |
| keyword |
= |
{Gaussian Field, DT-caracteristic, Forest fires} |
| } |
|
12 - 3D city modeling based on Hidden Markov Model.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), San Antonio, U.S., September 2007.
Note : Copyright IEEE
Keywords : 3D reconstruction, Building, Hidden Markov Model.
@INPROCEEDINGS{lafarge_icip07,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{3D city modeling based on Hidden Markov Model}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{San Antonio, U.S.}, |
| note |
= |
{Copyright IEEE}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4379207}, |
| keyword |
= |
{3D reconstruction, Building, Hidden Markov Model} |
| } |
|
13 - Rectangular Road Marking Detection with Marked Point Processes.
O. Tournaire and N. Paparoditis and F. Lafarge. In ISPRS Conference Photogrammetric Image Analysis (PIA), Vol. 36, pages 149--154, Org. IAPRS, Munich, Germany, September 2007.
@INPROCEEDINGS{tournaire_pia07,
|
| author |
= |
{Tournaire, O. and Paparoditis, N. and Lafarge, F.}, |
| title |
= |
{Rectangular Road Marking Detection with Marked Point Processes}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{ISPRS Conference Photogrammetric Image Analysis (PIA)}, |
| volume |
= |
{36}, |
| pages |
= |
{149--154}, |
| organization |
= |
{IAPRS}, |
| address |
= |
{Munich, Germany}, |
| pdf |
= |
{http://www-sop.inria.fr/ariana/Publis/2007-tournaire-pia.pdf}, |
| keyword |
= |
{} |
| } |
|
14 - An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), Atlanta, October 2006.
Keywords : 3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images.
Copyright : IEEE
@INPROCEEDINGS{lafarge_icip06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Atlanta}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_icip06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images} |
| } |
|
15 - Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS), Marne La Vallee, France, July 2006.
Keywords : 3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations.
@INPROCEEDINGS{lafarge_isprs06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_isprs06.pdf}, |
| keyword |
= |
{3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations} |
| } |
|
16 - An Automatic 3D City Model : a Bayesian Approach using Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 2006.
Note : Copyright IEEE
Keywords : 3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM).
@INPROCEEDINGS{florenticassp06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic 3D City Model : a Bayesian Approach using Satellite Images}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Toulouse, France}, |
| note |
= |
{Copyright IEEE}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_florenticassp06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM)} |
| } |
|
17 - 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} |
| } |
|
18 - 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} |
| } |
|
top of the page
5 Technical and Research Reports |
1 - A structural approach for 3D building reconstruction.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 6048, INRIA, November 2006.
Keywords : 3D reconstruction, Structural approach, Building, RJMCMC, Viterbi.
@TECHREPORT{Lafarge_rr_6048,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A structural approach for 3D building reconstruction}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6048}, |
| url |
= |
{https://hal.inria.fr/inria-00114338}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Lafarge_rr_6048.pdf}, |
| keyword |
= |
{3D reconstruction, Structural approach, Building, RJMCMC, Viterbi} |
| } |
|
2 - An automatic building extraction method : Application to the 3D-city modeling.
F. Lafarge and P. Trontin and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5925, INRIA, France, May 2006.
Keywords : Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM).
@TECHREPORT{lafarge_rr_may06,
|
| author |
= |
{Lafarge, F. and Trontin, P. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An automatic building extraction method : Application to the 3D-city modeling}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5925}, |
| address |
= |
{France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_rr_may06.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM)} |
| } |
|
3 - A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5687, INRIA, France, September 2005.
Keywords : 3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM).
@TECHREPORT{5687,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5687}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070326/fr/}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70326/filename/RR-5687.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/03/26/PS/RR-5687.ps}, |
| keyword |
= |
{3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM)} |
| } |
Résumé :
Dans ce rapport, nous développons un modèle paramétrique pour la reconstruction automatique de bâtiments en 3D fondé sur une approche bayésienne à partir de simulations PLEIADES. Les images satellitaires haute résolution représentent un nouveau type de données permettant de traiter les problèmes de reconstruction 3D de bâtiments. Leur résolution ``relativement basse'' et leur faible rapport signal sur bruit pour ce type de problèmes ne permet pas l'utilisation des méthodes standard développées dans le cas des images aériennes. Nous proposons une approche paramétrique utilisant des Modèles Numériques d'Elévation (MNE) et les empreintes de bâtiments associées modélisées par rectangles. La méthode proposée est fondée sur une approche bayésienne. Une technique de type de Monte Carlo par Chaînes de Markov est utilisée afin d'optimiser le modèle énergétique. |
Abstract :
This report develops a parametric model for automatic 3D building reconstruction based on a Bayesian approach from PLEIADES simulations. High resolution satellite images are a new kind of data to deal with 3D building reconstruction problems. Their ``relatively low'' resolution and low signal noise ration do not allow to use standard methods developed for the aerial image case. We propose a parametric approach using Digital Elevation Models (DEM) and associated rectangular building footprints. The proposed method is based on a Bayesian approach. A Markov Chain Monte Carlo technique is used to optimize the energy model. |
|
4 - Détection de Feux de Forêt par Analyse Statistique de la Radiométrie d'Images Satellitaires.
F. Lafarge and X. Descombes and J. Zerubia. Research Report 5369, INRIA, France, December 2004.
Keywords : Forest fires, Gaussian Field, Rare event.
@TECHREPORT{5369,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Détection de Feux de Forêt par Analyse Statistique de la Radiométrie d'Images Satellitaires}, |
| year |
= |
{2004}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5369}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070634}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70634/filename/RR-5369.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/06/34/PS/RR-5369.ps}, |
| keyword |
= |
{Forest fires, Gaussian Field, Rare event} |
| } |
Résumé :
Nous proposons, dans ce rapport, une méthode de détection des feux de forêt par imagerie satellitaire fondée sur la théorie des champs aléatoires. L'idée consiste à modéliser l'image par une réalisation d'un champ gaussien afin d'en extraire, par une analyse statistique, les éléments étrangers pouvant correspondre aux feux.
Le canal IRT (InfraRouge Thermique) contient des longueurs d'onde particulièrement sensibles à l'émission de chaleur. L'intensité d'un pixel d'une image IRT est donc d'autant plus forte que la température de la zone associée à ce pixel est élevée. Les feux de forêt peuvent alors être caractérisés par des pics d'intensité sur ce type d'images. Nous proposons une méthode de classification non supervisée et automatique fondée sur la théorie des champs gaussiens. Pour ce faire, nous modélisons dans un premier temps l'image par une réalisation d'un champ gaussien. Les zones de feux, minoritaires et de fortes intensités sont considérées comme des éléments étrangers à ce champ : ce sont des évènements rares. Ensuite, par une analyse statistique, nous déterminons un jeu de probabilités définissant, pour une zone donnée de l'image, un degré d'appartenance au champ gaussien, et par complémentarité aux zones potentiellement en feux. |
Abstract :
We present in this report a method for forest fire detection in satellite images based on random field theory. The idea is to model the image as a realization of a gaussian field in order to extract the rare events, which are potential fires, by a statistical analysis.
The TIR (Thermical InfraRed) channel has a wavelength sensitive to the emission of heat : the higher the heat of a area, the higher the intensity of the corresponding pixel of the image. Then a forest fire can be characterized by peak intensity in TIR images. We present an fully automatic unsupervised classification method based on Gaussian field theory. First we model the image as a realization of a Gaussian field. The fire areas, which have high intensity and are supposed to be a minority, are considered as foreign elements of that field : they are rare events. Then we determine by a statistical analysis a set of probabilities which characterizes the degree of belonging to the Gaussian field of a small area of the image. So, we estimate the probability that the area is a potential fire. |
|
5 - Noyaux Texturaux pour les Problèmes de Classification par SVM en Télédétection.
F. Lafarge and X. Descombes and J. Zerubia. Research Report 5370, INRIA, France, December 2004.
Keywords : Support Vector Machines, Classification, Forest fires, Urban areas, Learning base, Markov Fields.
@TECHREPORT{5370,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Noyaux Texturaux pour les Problèmes de Classification par SVM en Télédétection}, |
| year |
= |
{2004}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5370}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070633}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70633/filename/RR-5370.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/06/33/PS/RR-5370.ps}, |
| keyword |
= |
{Support Vector Machines, Classification, Forest fires, Urban areas, Learning base, Markov Fields} |
| } |
Résumé :
Nous détaillons dans ce rapport la construction de deux noyaux texturaux s'utilisant dans les problèmes de classification par «Support Vector Machines» en télédétection. Les SVM constituent une méthode de classification supervisée particulièrement bien adaptée pour traiter des données de grande dimension telles que les images satellitaires. Par cette méthode, nous souhaitons réaliser l'apprentissage de paramètres qui permettent la différenciation entre deux ensembles de pixels connexes non-identiques. Nous travaillons pour cela sur des fonctions noyaux, fonctions caractérisant une certaine similarité entre deux données. Dans notre cas, cette similarité sera fondée à la fois sur une notion radiométrique et sur une notion texturale. La principale difficulté rencontrée dans cette étude réside dans l'élaboration de paramètres texturaux pertinents qui modélisent au mieux l'homogénéité d'un ensemble de pixels connexes. Nous appliquons les noyaux proposés à deux problèmes de télédétection: la détection de feux de forêt et la détection de zones urbaines à partir d'images satellitaires haute résolusion. |
Abstract :
We present in this report two textural kernels for «Support Vector Machines» classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We would like to learn parameters which allow the differentiation between two sets of connected pixels. We also introduce kernel functions which characterize a notion of similarity between two pieces of data. In our case this similarity is based on a radiometric charateristic and a textural characteristic. The main difficulty is to elaborate textural parameters which are pertinent and characterize as well as possible the homogeneity of a set of connected pixels. We apply this method to remote sensing problems : the detection of forest fires and the extraction of urban areas in high resolution satellite images. |
|
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