|
|
Publications of 2003
Result of the query in the list of publications :
7 Articles |
1 - A Multiresolution Approach for Shape from Shading Coupling Deterministic and Stochastic Optimization.
A. Crouzil and X. Descombes and J.D. Durou. IEEE Trans. Pattern Analysis ans Machine Intelligence, 25(11): pages 1416--1421, November 2003.
Note : Special section on `Energy minimization methods in computer vision and pattern recognition'
@ARTICLE{crouzilXDJDD,
|
| author |
= |
{Crouzil, A. and Descombes, X. and Durou, J.D.}, |
| title |
= |
{A Multiresolution Approach for Shape from Shading Coupling Deterministic and Stochastic Optimization}, |
| year |
= |
{2003}, |
| month |
= |
{November}, |
| journal |
= |
{IEEE Trans. Pattern Analysis ans Machine Intelligence}, |
| volume |
= |
{25}, |
| number |
= |
{11}, |
| pages |
= |
{1416--1421}, |
| note |
= |
{Special section on `Energy minimization methods in computer vision and pattern recognition'}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/34/27807/01240116.pdf?tp=&arnumber=1240116&isnumber=27807}, |
| keyword |
= |
{} |
| } |
|
2 - Wavelet-based Level Set Evolution for Classification of Textured Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud. IEEE Trans. Image Processing, 12(12), 2003.
@ARTICLE{aujolGL,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Wavelet-based Level Set Evolution for Classification of Textured Images}, |
| year |
= |
{2003}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{12}, |
| number |
= |
{12}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28122/01257399.pdf?tp=&arnumber=1257399&isnumber=28122}, |
| keyword |
= |
{} |
| } |
|
3 - Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 170: pages 13--22, 2003.
@ARTICLE{lacostesfpt,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet}, |
| year |
= |
{2003}, |
| journal |
= |
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, |
| volume |
= |
{170}, |
| pages |
= |
{13--22}, |
| url |
= |
{http://www.researchgate.net/profile/Nicolas_Baghdadi/publication/236882132_Extraction_automatique_des_rseaux_liniques__partir_dimages_satellitaires_et_ariennes_par_processus_Markov_objets/links/00463519e05ebd9e83000000.pdf?disableCoverPage=true}, |
| keyword |
= |
{} |
| } |
|
4 - Droplet Shapes for a Class of Models in Z^2 at Zero Temperature.
X. Descombes and E. Pechersky. Journal of Statistical Physics, 111(1-2): pages 129--169, 2003.
@ARTICLE{descombesEP,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Droplet Shapes for a Class of Models in Z^2 at Zero Temperature}, |
| year |
= |
{2003}, |
| journal |
= |
{Journal of Statistical Physics}, |
| volume |
= |
{111}, |
| number |
= |
{1-2}, |
| pages |
= |
{129--169}, |
| pdf |
= |
{http://link.springer.com/article/10.1023/A%3A1022252923753}, |
| keyword |
= |
{} |
| } |
|
5 - Classification de Textures Hyperspectrales Fondée sur un Modèle Markovien et Une Technique de Poursuite de Projection.
G. Rellier and X. Descombes and F. Falzon and J. Zerubia. Traitement du Signal, 20(1): pages 25--42, 2003.
@ARTICLE{rellierXDFFJZ,
|
| author |
= |
{Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, |
| title |
= |
{Classification de Textures Hyperspectrales Fondée sur un Modèle Markovien et Une Technique de Poursuite de Projection}, |
| year |
= |
{2003}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{20}, |
| number |
= |
{1}, |
| pages |
= |
{25--42}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/2216}, |
| keyword |
= |
{} |
| } |
|
6 - Satellite image deblurring using complex wavelet packets.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. International Journal of Computer Vision, 51(3): pages 205--217, 2003.
@ARTICLE{JalobeaLBFJZ,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Satellite image deblurring using complex wavelet packets}, |
| year |
= |
{2003}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{51}, |
| number |
= |
{3}, |
| pages |
= |
{205--217}, |
| pdf |
= |
{http://link.springer.com/article/10.1023/A%3A1021801918603}, |
| keyword |
= |
{} |
| } |
|
7 - Skewed alpha-stable distributions for modelling textures.
E.E. Kuruoglu and J. Zerubia. Pattern Recognition Letters, 24(1-3): pages 339--348, 2003.
@ARTICLE{Kuruoglu03a,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Skewed alpha-stable distributions for modelling textures}, |
| year |
= |
{2003}, |
| journal |
= |
{Pattern Recognition Letters}, |
| volume |
= |
{24}, |
| number |
= |
{1-3}, |
| pages |
= |
{339--348}, |
| url |
= |
{http://www.sciencedirect.com/science/article/pii/S0167865502002477}, |
| keyword |
= |
{} |
| } |
|
 top of the page
3 PhD Thesis and Habilitations |
1 - Analyse du milieu urbain par une approche de fusion de données satellitaires optiques et radar.
O. Viveros-Cancino. PhD Thesis, Universite de Nice Sophia Antipolis, 2003.
@PHDTHESIS{Viveros-Cancino03,
|
| author |
= |
{Viveros-Cancino, O.}, |
| title |
= |
{Analyse du milieu urbain par une approche de fusion de données satellitaires optiques et radar}, |
| year |
= |
{2003}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{http://www-sop.inria.fr/dias/Theses/phd-8.php}, |
| keyword |
= |
{} |
| } |
|
2 - A Probabilistic Framework for Adaptive Texture Description.
K. Brady. PhD Thesis, Universite de Nice Sophia Antipolis, 2003.
@PHDTHESIS{Brady03t,
|
| author |
= |
{Brady, K.}, |
| title |
= |
{A Probabilistic Framework for Adaptive Texture Description}, |
| year |
= |
{2003}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{Theses/these-kbrady-2003.pdf}, |
| keyword |
= |
{} |
| } |
|
3 - Modèles Variationnels et Équations aux Dérivées Partielles pour le Déroulement de Phase en Interférométrie Radar de Type RSO.
C. Lacombe. PhD Thesis, Universite de Nice Sophia Antipolis, 2003.
Note : papier (tu-0816)
@PHDTHESIS{Lacombe03,
|
| author |
= |
{Lacombe, C.}, |
| title |
= |
{Modèles Variationnels et Équations aux Dérivées Partielles pour le Déroulement de Phase en Interférométrie Radar de Type RSO}, |
| year |
= |
{2003}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| note |
= |
{papier (tu-0816)}, |
| keyword |
= |
{} |
| } |
|
top of the page
22 Conference articles |
1 - Extraction automatique de caricatures de bâtiments sur des Modèles Numériques d'Elèvation.
M. Ortner and X. Descombes and J. Zerubia. In Pixels et Cités, ENSG, Marne la Vallée, France, November 2003.
@INPROCEEDINGS{mathiaspix,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction automatique de caricatures de bâtiments sur des Modèles Numériques d'Elèvation}, |
| year |
= |
{2003}, |
| month |
= |
{November}, |
| booktitle |
= |
{Pixels et Cités}, |
| address |
= |
{ENSG, Marne la Vallée, France}, |
| pdf |
= |
{Articles/PixelsCites2003.pdf}, |
| keyword |
= |
{} |
| } |
|
2 - Un Nouveau Modèle Pour L'extraction de Caricatures de Bâtiments sur Des Modèles Numériques D'Élèvation.
M. Ortner and X. Descombes and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), Hammamet, Tunisia, October 2003.
@INPROCEEDINGS{mathiastaima,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Un Nouveau Modèle Pour L'extraction de Caricatures de Bâtiments sur Des Modèles Numériques D'Élèvation}, |
| year |
= |
{2003}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, |
| address |
= |
{Hammamet, Tunisia}, |
| url |
= |
{http://conferences.telecom-bretagne.eu/taima/_2003/}, |
| keyword |
= |
{} |
| } |
|
3 - Wavelet-Based Superresolution in Astronomy.
R. Willett and I. H. Jermyn and R. Nowak and J. Zerubia. In Proc. Astronomical Data Analysis Software and Systems, Strasbourg, France, October 2003.
Keywords : Superresolution, Wavelets, Astronomy.
@INPROCEEDINGS{Willett03,
|
| author |
= |
{Willett, R. and Jermyn, I. H. and Nowak, R. and Zerubia, J.}, |
| title |
= |
{Wavelet-Based Superresolution in Astronomy}, |
| year |
= |
{2003}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. Astronomical Data Analysis Software and Systems}, |
| address |
= |
{Strasbourg, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Willett03adass.pdf}, |
| keyword |
= |
{Superresolution, Wavelets, Astronomy} |
| } |
Abstract :
High-resolution astronomical images can be reconstructed
from several blurred and noisy low-resolution images using a computational
process known as superresolution reconstruction. Superresolution
reconstruction is closely related to image deconvolution, except that the
low-resolution images are not registered and their relative translations
and rotations must be estimated in the process. The novelty of our approach
to the superresolution problem is the use of wavelets and related
multiresolution methods within an expectation-maximization reconstruction
process to improve the accuracy and visual quality of the reconstructed
image. Simulations demonstrate the effectiveness of the proposed
method, including its ability to distinguish between tightly grouped stars
with a small set of observations. |
|
4 - Higher Order Active Contours and their Application to the Detection of Line Networks in Satellite Imagery.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE Workshop Variational, Geometric and Level Set Methods in Computer Vision, at ICCV, Nice, France, October 2003.
Keywords : Higher-order, Active contour, Shape, Road network, Segmentation, Prior.
@INPROCEEDINGS{Rochery03a,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Higher Order Active Contours and their Application to the Detection of Line Networks in Satellite Imagery}, |
| year |
= |
{2003}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE Workshop Variational, Geometric and Level Set Methods in Computer Vision}, |
| address |
= |
{at ICCV, Nice, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_vlsm03.pdf}, |
| keyword |
= |
{Higher-order, Active contour, Shape, Road network, Segmentation, Prior} |
| } |
Abstract :
We present a novel method for the incorporation of shape information
into active contour models, and apply it to the extraction
of line networks (e.g. road, water) from satellite imagery.
The method is based on a new class of contour energies.
These energies are quadratic on the space of one-chains
in the image, as opposed to classical energies, which are linear.
They can be expressed as double integrals on the contour,
and thus incorporate non-trivial interactions between
different contour points. The new energies describe families
of contours that share complex geometric properties, without
making reference to any particular shape. Networks fall
into such a family, and to model them we make a particular
choice of quadratic energy whose minima are reticulated.
To optimize the energies, we use a level set approach. The
forces derived from the new energies are non-local however,
thus necessitating an extension of standard level set methods.
Promising experimental results are obtained using real
images. |
|
5 - Décomposition D'images: Application Aux Images RSO.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
@INPROCEEDINGS{jf_gretsi,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Décomposition D'images: Application Aux Images RSO}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13577}, |
| keyword |
= |
{} |
| } |
|
6 - Wavelet-Based Level Set Evolution for Classification of Textured Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
@INPROCEEDINGS{jf_icip,
|
| author |
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{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
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{Wavelet-Based Level Set Evolution for Classification of Textured Images}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona, Spain}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1246863}, |
| keyword |
= |
{} |
| } |
|
7 - Texture Analysis: An Adaptive Probabilistic Approach.
K. Brady and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
Keywords : Adaptive, Wavelet packet, Statistics, Texture.
@INPROCEEDINGS{Brady03,
|
| author |
= |
{Brady, K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture Analysis: An Adaptive Probabilistic Approach}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona, Spain}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03icip.pdf}, |
| keyword |
= |
{Adaptive, Wavelet packet, Statistics, Texture} |
| } |
Abstract :
Two main issues arise when working in the area of texture
segmentation: the need to describe the texture accurately by
capturing its underlying structure, and the need to perform
analyses on the boundaries of textures. Herein, we tackle
these problems within a consistent probabilistic framework.
Starting from a probability distribution on the space of infinite
images, we generate a distribution on arbitrary finite
regions by marginalization. For a Gaussian distribution, the
computational requirement of diagonalization and the modelling
requirement of adaptivity together lead naturally to
adaptive wavelet packet models that capture the ‘significant
amplitude features’ in the Fourier domain. Undecimated
versions of the wavelet packet transform are used to diagonalize
the Gaussian distribution efficiently, albeit approximately.
We describe the implementation and application of
this approach and present results obtained on several Brodatz
texture mosaics. |
|
8 - Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
@INPROCEEDINGS{lacosteXDJZNB03,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13529}, |
| keyword |
= |
{} |
| } |
|
9 - Road Network Extraction in Remote Sensing by a Markov Object Process.
C. Lacoste and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
@INPROCEEDINGS{lacosteXDJZ03,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Road Network Extraction in Remote Sensing by a Markov Object Process}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona, Spain}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1247420}, |
| keyword |
= |
{} |
| } |
|
10 - Filtering interferometric phase images by anisotropic diffusion.
C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, September 2003.
@INPROCEEDINGS{ClacombGALBFPK,
|
| author |
= |
{Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, |
| title |
= |
{Filtering interferometric phase images by anisotropic diffusion}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Barcelona}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1247201}, |
| keyword |
= |
{} |
| } |
|
11 - Étude D'une Nouvelle Classe de Contours Actifs Pour la Détection de Routes Dans Des Images de Télédétection.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
@INPROCEEDINGS{Rochery03,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Étude D'une Nouvelle Classe de Contours Actifs Pour la Détection de Routes Dans Des Images de Télédétection}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_gretsi03.pdf}, |
| keyword |
= |
{} |
| } |
|
12 - Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images.
K. Brady and I. H. Jermyn and J. Zerubia. In Proc. British Machine Vision Conference (BMVC), Norwich, U. K., September 2003.
Keywords : probabilistic, Adaptive, wavelet, Texture.
@INPROCEEDINGS{Brady03a,
|
| author |
= |
{Brady, K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Adaptive Probabilistic Models of Wavelet Packets for the Analysis and Segmentation of Textured Remote Sensing Images}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Norwich, U. K.}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Brady03bmvc.pdf}, |
| keyword |
= |
{probabilistic, Adaptive, wavelet, Texture} |
| } |
Abstract :
Remote sensing imagery plays an important role in many elds. It has
become an invaluable tool for diverse applications ranging from cartography
to ecosystem management. In many of the images processed in these types
of applications, semantic entities in the scene are correlated with textures
in the image. In this paper, we propose a new method of analysing such
textures based on adaptive probabilistic models of wavelet packets. Our approach
adapts to the principal periodicities present in the textures, and can
capture long-range correlations while preserving the independence of the
wavelet packet coefcients. This technique has been applied to several remote
sensing images, the results of which are presented. |
|
13 - Natural image modeling using complex wavelets.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. SPIE Conference on Wavelets, Vol. 5207, San Diego, August 2003.
@INPROCEEDINGS{Jalobfjz,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Natural image modeling using complex wavelets}, |
| year |
= |
{2003}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. SPIE Conference on Wavelets}, |
| volume |
= |
{5207}, |
| address |
= |
{San Diego}, |
| url |
= |
{http://spie.org/Publications/Proceedings/Paper/10.1117/12.507945}, |
| keyword |
= |
{} |
| } |
|
14 - Discrete Wavelet transforms that have an adaptive low pass filter.
G.C.K. Abhayaratne. In Proc. International Symposium on Signal Processing and its Applications (ISSPA), Paris, France, July 2003.
@INPROCEEDINGS{gcka3,
|
| author |
= |
{Abhayaratne, G.C.K.}, |
| title |
= |
{Discrete Wavelet transforms that have an adaptive low pass filter}, |
| year |
= |
{2003}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Symposium on Signal Processing and its Applications (ISSPA)}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1224920}, |
| keyword |
= |
{} |
| } |
|
15 - Remotely sensed image segmentation using an object point process.
S. Drot and H. Le Men and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, July 2003.
@INPROCEEDINGS{drot,
|
| author |
= |
{Drot, S. and Le Men, H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Remotely sensed image segmentation using an object point process}, |
| year |
= |
{2003}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1294331}, |
| keyword |
= |
{} |
| } |
|
16 - Urban Scene Rendering using Object Description.
F. Cerdat and X. Descombes and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, July 2003.
@INPROCEEDINGS{fcerdat,
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| author |
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{Cerdat, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Urban Scene Rendering using Object Description}, |
| year |
= |
{2003}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1293679}, |
| keyword |
= |
{} |
| } |
|
17 - Decomposing an Image: Application to SAR Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. In Proc. Scale-Space, Vol. 2695, series Lecture No, June 2003.
@INPROCEEDINGS{jf_scalespace,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
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{Decomposing an Image: Application to SAR Images}, |
| year |
= |
{2003}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. Scale-Space}, |
| volume |
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{2695}, |
| series |
= |
{Lecture No}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-44935-3_21}, |
| keyword |
= |
{} |
| } |
|
18 - Diffusion anisotrope et filtrage interférométrique.
C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Congrès National d'Analyse Numérique, Montpellier, June 2003.
@INPROCEEDINGS{Lacombe-Canum03,
|
| author |
= |
{Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, |
| title |
= |
{Diffusion anisotrope et filtrage interférométrique}, |
| year |
= |
{2003}, |
| month |
= |
{June}, |
| booktitle |
= |
{Congrès National d'Analyse Numérique}, |
| address |
= |
{Montpellier}, |
| url |
= |
{http://www.i3m.univ-montp2.fr/CANUM2003/}, |
| pdf |
= |
{http://www.i3m.univ-montp2.fr/CANUM2003/COMMUNICATIONS/CONF_ORALES/caroline.lacombe.pdf}, |
| keyword |
= |
{} |
| } |
|
19 - Filtrage adaptatif des interférogrammes par diffusion anisotrope.
C. Lacombe and G. Aubert and L. Blanc-Féraud and P. Kornprobst. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Gerardmer, May 2003.
@INPROCEEDINGS{CLGALBFPK,
|
| author |
= |
{Lacombe, C. and Aubert, G. and Blanc-Féraud, L. and Kornprobst, P.}, |
| title |
= |
{Filtrage adaptatif des interférogrammes par diffusion anisotrope}, |
| year |
= |
{2003}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Gerardmer}, |
| ps |
= |
{http://www-math.unice.fr/publis/clacombe_orasis03.ps}, |
| keyword |
= |
{} |
| } |
|
20 - Gaussian Mixture Models of Texture and Colour for Image Database Retrieval.
H. Permuter and J.M. Francos and I. H. Jermyn. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, April 2003.
Keywords : Texture, Gaussian mixture, Classification, Aerial images.
@INPROCEEDINGS{Permuter03,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{Gaussian Mixture Models of Texture and Colour for Image Database Retrieval}, |
| year |
= |
{2003}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Hong Kong}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Permuter03icassp.pdf}, |
| keyword |
= |
{Texture, Gaussian mixture, Classification, Aerial images} |
| } |
Abstract :
We introduce Gaussian mixture models of ‘structure’ and
colour features in order to classify coloured textures in images,
with a view to the retrieval of textured colour images
from databases. Classifications are performed separately
using structure and colour and then combined using
a confidence criterion. We apply the models to the VisTex
database and to the classification of man-made and natural
areas in aerial images. We compare these models with others
in the literature, and show an overall improvement in
performance. |
|
21 - Classification d'images par approche variationnelle.
L. Blanc-Féraud. In Workshop on Vision, Image, and Agriculture, Dijon, January 2003.
Note : Invited talk.
@INPROCEEDINGS{LaureBF,
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| author |
= |
{Blanc-Féraud, L.}, |
| title |
= |
{Classification d'images par approche variationnelle}, |
| year |
= |
{2003}, |
| month |
= |
{January}, |
| booktitle |
= |
{Workshop on Vision, Image, and Agriculture}, |
| address |
= |
{Dijon}, |
| note |
= |
{Invited talk.}, |
| keyword |
= |
{} |
| } |
|
22 - Building Extraction from Digital Elevation Model..
M. Ortner and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Honk Kong, 2003.
@INPROCEEDINGS{mathiasicassp,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction from Digital Elevation Model.}, |
| year |
= |
{2003}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Honk Kong}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1199477}, |
| keyword |
= |
{} |
| } |
|
top of the page
13 Technical and Research Reports |
1 - Contours Actifs d'Ordre Supérieur Appliqués à la Détection de Linéiques dans des Images de Télédétection.
M. Rochery and I. H. Jermyn and J. Zerubia. Research Report 5063, INRIA, France, December 2003.
Keywords : Line networks, Active contour, Deformable models, Object extraction.
@TECHREPORT{RRRochery03,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Contours Actifs d'Ordre Supérieur Appliqués à la Détection de Linéiques dans des Images de Télédétection}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5063}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071521}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71521/filename/RR-5063.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/21/PS/RR-5063.ps}, |
| keyword |
= |
{Line networks, Active contour, Deformable models, Object extraction} |
| } |
Résumé :
Dans ce rapport, nous présentons une nouvelle méthode pour l'incorporation d'une information sur la géométrie a priori dans le cadre des contours actifs. Nous introduisons une nouvelle classe de contours actifs d'ordre supérieur, qui sont des énergies quadratiques sur l'espace des 1-chaînes, contrairement aux énergies classiquement utilisées qui sont linéaires. Ces énergies permettent de définir des interactions non triviales entre les différents points du contour. Elles donnent naissance à des forces non locales, permettant ainsi d'introduire une information géométrique forte dans le modèle. D'un point de vue algorithmique, nous utilisons la méthodologie par courbes de niveau afin de trouver le minimum de l'énergie, la présence de forces non locales nécessitant une extension des méthodes standard utilisées pour l'évolution que nous décrivons. Nous utilisons ce nouveau modèle pour la détection de linéiques (routes, rivières, ...) dans les images de télédétection et nous montrons des résultats d'extraction sur des images réelles. |
Abstract :
In this report, we introduce a new class of active contour energies, quadratic on the space of 1-chains, as opposed to classical energies, which are linear. These energies define non trivial interactions between different points of the contour, and thus allow the incorporation of a priori shape information through the generation of non-local forces that carry geometric information. They also allow the definition of complex data terms linking the data at different points of the contour. To solve the models, we use the level set methodology, in the process extending the standard evolution methods to deal with the non-locality of the forces involved. We use this new approach in order to define models for the extraction of line networks (roads, rivers, ...) in satellite imagery. We show some results on real-world images. |
|
2 - A Multiresolution Approach for Shape from Shading Coupling Deterministic and Stochastic Optimization.
A. Crouzil and X. Descombes and J.D. Durou. Research Report 5006, INRIA, France, December 2003.
Keywords : Shape from shading, Simulated Annealing, Optimization, Multiresolution.
@TECHREPORT{Crouzil03,
|
| author |
= |
{Crouzil, A. and Descombes, X. and Durou, J.D.}, |
| title |
= |
{A Multiresolution Approach for Shape from Shading Coupling Deterministic and Stochastic Optimization}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5006}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071578}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71578/filename/RR-5006.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/78/PS/RR-5006.ps}, |
| keyword |
= |
{Shape from shading, Simulated Annealing, Optimization, Multiresolution} |
| } |
Résumé :
Le Shape from shading est un problème inverse mal posé pour lequel aucune méthode de résolution complètement satisfaisante n'a encore été proposée. Dans ce rapport technique, nous ramenons le à un problème d'optimisation. Nous montrons d'abord que l'approche déterministe fournit des algorithmes efficaces en termes de temps de calcul, mais est d'un intérêt limité lorsque l'énergie comporte des minima locaux très profonds. Nous proposons comme alternative une approche stochastique utilisant le recuit simulé. Les résultats obtenus dépassent largement ceux de l'approche déterministe. La contrepartie est l'extrême lenteur du processus d'optimisation. Pour cette raison, nous proposons une approche hybride qui combine les approches déterministe et stochastique dans un cadre de multi-résolution. |
Abstract :
Shape from shading is an ill-posed inverse problem for which there is no completely satisfactory solution in the existing literature. In this technical report, we address shape from shading as an energy minimization problem. We first show that the deterministic approach provides efficient algorithms in terms of CPU time, but reaches its limits since the energy associated to shape from shading can contain multiple deep local minima. We derive an alternative stochastic approach using simulated annealing. The obtained results strongly outperform the results of the deterministic approach. The shortcoming is an extreme slowness of the optimization. Therefore, we propose an hybrid approach which combines the deterministic and stochastic approaches in a multiresolution framework. |
|
3 - A Binary Tree-Structured MRF Model for Multispectral Satellite Image Segmentation.
G. Scarpa and G. Poggi and J. Zerubia. Research Report 5062, INRIA, France, December 2003.
Keywords : Bayesian estimation, Classification, Markov Fields, Hierarchical models.
@TECHREPORT{Scarpa03,
|
| author |
= |
{Scarpa, G. and Poggi, G. and Zerubia, J.}, |
| title |
= |
{A Binary Tree-Structured MRF Model for Multispectral Satellite Image Segmentation}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5062}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071522}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71522/filename/RR-5062.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/22/PS/RR-5062.ps}, |
| keyword |
= |
{Bayesian estimation, Classification, Markov Fields, Hierarchical models} |
| } |
Résumé :
Dans ce rapport, nous proposons un modèle markovien a priori structuré à arbre binaire (le TS-MRF) pour la segmentation d'images satellitaires multispectrales. Ce modèle permet de représenter un champ bidimensionnel par une séquence de champs de Markov binaires, chacun correspondant à un noeud de l'arbre. Pour avoir une bonne classification, on peut adapter le modèle TS-MRF à la structure intrinsèque des données, en définissant un MRF, à plusieurs paramètres, très flexible. Bien que l'on définisse le modèle global sur tout l'arbre, l'optimisation et l'estimation peuvent être poursuivis en considérant un noeud à la fois, à partir de la racine jusqu'aux feuilles, avec une réduction significative de la complexité. En effet, on a montré expérimentalement que l'algorithme global est beaucoup plus rapide qu'un algorithme conventionnel fondé sur le modèle markovien d'Ising, en particulier quand le nombre des bandes spectrales est très grand. Grâce à la procédure d'optimisation séquentielle, ce modèle permet aussi de déterminer le nombre des classes présentes dans l'image satellitaire, dans le cadre d'une classification non supervisée, à travers une condition d'arrêt définie localement pour chaque noeud. Nous avons effectué des expériences sur une image SPOT de la baie de Lannion, pour laquelle nous disposons d'une vérité terrain, et nous avons trouvé que le modèle proposé fournit de meilleurs résultats que certains autres modèles de Markov et que d'autres méthodes variationnelles. |
Abstract :
In this work we detail a tree-structured MRF (TS-MRF) prior model useful for segmentation of multispectral satellite images. This model allows a hierarchical representation of a 2-D field by the use of a sequence of binary MRFs, each corresponding to a node in the tree. In order to get good performances, one can fit the intrinsic structure of the data to the TS-MRF model, thereby defining a multi-parameter, flexible, MRF. Although a global MRF model is defined on the whole tree, optimization as well estimation can be carried out by working on a single node at a time, from the root down to the leaves, with a significant reduction in complexity. Indeed the overall algorithm is proved experimentally to be much faster than a comparable algorithm based on a conventional Ising MRF model, especially when the number of bands becomes very large. Thanks to the sequential optimization procedure, this model also addresses the cluster validation problem of unsupervised segmentation, through the use of a stopping condition local to each node. Experiments on a SPOT image of the Lannion Bay, a ground-truth of which is available, prove the superior performance of the algorithm w.r.t. some other MRF based algorithms for supervised segmentation, as well as w.r.t. some variational methods. |
|
4 - Flattening of 3D Data.
R. Acar and B.W. Seales. Research Report 5048, INRIA, France, December 2003.
Keywords : Digital conservation, Document analysis, Restoration.
@TECHREPORT{Acar03,
|
| author |
= |
{Acar, R. and Seales, B.W.}, |
| title |
= |
{Flattening of 3D Data}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5048}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071535}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71535/filename/RR-5048.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/35/PS/RR-5048.ps}, |
| keyword |
= |
{Digital conservation, Document analysis, Restoration} |
| } |
Résumé :
Le but du projet de la bibliothèque numérique est de numériser les collections spéciales des bibliothèques; ceci consiste à transformer en données binaires des photographies du contenu de manuscripts rares ou anciens. L'objet, typiquement, n'est pas dans un plan. On enregistre, en même temps que des photographies de l'objet non plat et du texte déformé qui s'y trouve, la forme et la position de sa surface en utilisant un laseromètre. La manière de se servir de cette information pour enlever la distortion de la photographie avant d'enregistrer l'image numérique est alors un problème mathématique. Nous en examinons une formulation variationnelle et l'implantation correspondante. |
Abstract :
The digital library project strives to digitise special collections of libraries; this consists in storing as binary data, photographs of the content of ancient or rare manuscripts. The object is typically not in a flat plane. One collects, along with the photograph of the unflattened object (and the inevitably distorted text), a positional reading of its surface using laserometer. It is then a mathematical problem of how to use the latter information to undo the distortion of the photograph before storing the digitised image. |
|
5 - Extraction de Houppiers par Processus Objet.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5037, INRIA, France, December 2003.
Keywords : Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process, RJMCMC.
@TECHREPORT{Perrin03,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction de Houppiers par Processus Objet}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5037}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071547}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71547/filename/RR-5037.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/47/PS/RR-5037.ps}, |
| keyword |
= |
{Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process, RJMCMC} |
| } |
Résumé :
Nous cherchons à extraire des houppiers à partir d'images de télédétection. Pour ce faire, nous construisons un processus objet et assimilons nos images d'arbres à des réalisations de ce processus. La première étape consiste à définir d'une part les objets géométriques modélisant les arbres, et d'autre part la densité du processus à simuler.La seconde étape consiste à construire un algorithme MCMC à sauts réversibles, et une estimée de la configuration d'objets. Les transitions aléatoires de la chaîne sont régies par des noyaux de propositions, chacun étant associé à une perturbation.Nous testons notre modèle sur des images aériennes de peupleraies fournies par l'IFN. |
Abstract :
In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process.Then, we use a reversible jump MCMC dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crowns distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels.Results are shown on aerial images of poplars given by IFN. |
|
6 - Texture-adaptive mother wavelet selection for texture analysis.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. Research Report, INRIA, France, December 2003.
@TECHREPORT{Abhayaratne,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture-adaptive mother wavelet selection for texture analysis}, |
| year |
= |
{2003}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/hal-01208017}, |
| pdf |
= |
{Rapports/RR-8783.pdf}, |
| keyword |
= |
{} |
| } |
|
7 - A Probabilistic Framework for Adaptive Texture Description.
K. Brady and I. H. Jermyn and J. Zerubia. Research Report 4920, INRIA, France, September 2003.
Keywords : Segmentation, Texture, Wavelet packet.
@TECHREPORT{4920,
|
| author |
= |
{Brady, K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{A Probabilistic Framework for Adaptive Texture Description}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4920}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071659}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71659/filename/RR-4920.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/16/59/PS/RR-4920.ps}, |
| keyword |
= |
{Segmentation, Texture, Wavelet packet} |
| } |
Résumé :
Ce rapport présente le développement d'un nouveau cadre probabiliste cohérent pour la description adaptative de texture. En partant d'une distribution de probabilité sur un espace d'images infinies, nous générons une distribution sur des régions finies par marginalisation. Pour une distribution gaussienne, les contraintes de calcul imposées par la diagonalisation nous conduisent naturellement à des modèles utilisant des paquets d'ondelettes adaptatifs. Ces modèles reflètent les principales périodicités présentes dans les textures et permettent également d'avoir des corrélations à longue portée tout en préservant l'indépendance des coefficients des paquets d'ondelettes. Nous avons appliqué notre méthode à la segmentation. Deux types de données figurent dans notre ensemble de test: des mosaïques synthétiques de Brodatz et des images satellitaires haute résolution. Dans le cas des textures synthétiques, nous utilisons la version non-décimée de la transformée en paquets d'ondelettes afin de diagonaliser la distribution gaussienne de manière efficace, bien qu'approximative. Cela nous permet d'effectuer une classification de la mosaique pixel par pixel. Une étape de régularisation est ensuite effectuée afin d'arriver à un résultat de segmentation final plus lisse. Afin d'obtenir les meilleurs résultats possibles dans le cas de données réelles, la moyenne de la distribution est ensuite introduite dans le modèle. L'approximation faite pour la classification des mosaiques de textures synthetiques a été testée sur des images réelles, mais les résultats obtenus n'étaient pas satisfaisants. C'est pourquoi nous avons introduit, pour ce type de données, une technique de classification heuristique basée sur la transformée en paquets d'ondelettes décimée. Les résultats de segmentation sont ensuite régularisés à l'aide de la même méthode que dans le cas synthétique. Nous présentons les résultats pour chaque type de données et concluons par une discussion. |
Abstract :
This report details the development of a probabilistic framework for adaptive texture description. Starting with a probability distribution on the space of infinite images, we generate a distribution on finite regions by marginalisation. For a Gaussian distribution, the computational requirement of diagonalisation leads naturally to adaptive wavelet packet models which capture the principal periodicities present in the textures and allow long-range correlations while preserving the independence of the wavelet packet coefficients. These models are then applied to the task of segmentation. Two data types are included in our test bed: synthetic Brodatz mosaics and high-resolution satellite images. For the case of the synthetic textures, undecimated versions of the wavelet packet transform are used to diagonalise the Gaussian distribution efficiently, albeit approximately. This enables us to perform a pixelwise classification of the mosaics. A regularisation step is then implemented in order to arrive at a smooth final segmentation. In order to obtain the best possible results for the real dataset, the mean of the distribution is included in the model. The approximation made for the classification of the synthetic texture mosaics is tested on the remote sensing images, but it produces unsatisfactory results. Therefore we introduce a heuristic classification technique for this dataset, based on a decimated wavelet packet transform. The resulting segmentation is then regularised using the same method as in the synthetic case. Results are presented for both types of data and a discussion follows. |
|
8 - Automatic 3D Land Register Extraction from Altimetric Data in Dense Urban Areas.
M. Ortner and X. Descombes and J. Zerubia. Research Report 4919, INRIA, France, September 2003.
Keywords : Object extraction, Buildings, RJMCMC, Stochastic geometry, Digital Elevation Model (DEM), Marked point process.
@TECHREPORT{4919,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Automatic 3D Land Register Extraction from Altimetric Data in Dense Urban Areas}, |
| year |
= |
{2003}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4919}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071660}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71660/filename/RR-4919.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/16/60/PS/RR-4919.ps}, |
| keyword |
= |
{Object extraction, Buildings, RJMCMC, Stochastic geometry, Digital Elevation Model (DEM), Marked point process} |
| } |
Résumé :
Ce travail présente un algorithme qui extrait automatiquement un plan cadastral de la description altimétrique (relief) d'une zone urbaine dense. L'altimétrie d'une ville est une donnée qui est maintenant facilement accessible. Dans ce rapport, nous présentons par exemple des résultats sur deux types de données altimétriques : le premier consiste en un Modèle Numérique d'Elévation (MNE) obtenu par corrélation d'images optiques, le second correspond à un MNE obtenu par mesure LASER.Notre objectif principal est de définir un algorithme entièrement automatique capable d'extraire un grand nombre de bâtiments dans des zones urbaines denses.Nous nous intéressons donc plus particulièrement à l'extraction de formes élémentaires et proposons un algorithme qui modélise les bâtiments par des formes rectangulaires. Le résultat obtenu consiste en une carte cadastrale qui peut être utilisée pour faire une estimation précise des formes de toits, par exemple.L'algorithme proposé ici repose sur nos travaux précédents. Nous modélisons des villes par des configurations de rectangles auxquelles nous associons une énergie définie de manière à tenir compte aussi bien d'une information de bas niveau provenant des données utilisées que d'une connaissance géometrique de l'agencement des bâtiments dans les zones urbaines.L'estimation est ensuite faite en minimisant l'énergie définie grace à un recuit-simulé.Nous utilisons un échantilloneur MCMC qui est une combinaison de techniques générales de type Metropolis Hastings Green et de l'algorithme de simulation de processus ponctuel proposé par Geyer et Møller. Nous utilisons en particulier des noyaux de proposition originaux comme la naissance ou mort dans un voisinage, et nous définissons l'énergie par rapport à un processus ponctuel de Poisson non-homogène, ce qui permet d'améliorer le comportement dynamique de l'algorithme.Les resultats que nous présentons sont obtenus sur des donnée réelles fournies par l'IGN. Nous extrayons automatiquement des configurations composées d'une centaine de bâtiments sur des zones dont la taille est en moyenne de 200m sur 200m. L'erreur commise est en moyenne de 15. |
Abstract :
This work present an automatic algorithm that extract 3D land register from altimetric data in dense urban areas. Altimetry of a town is a data which is easily available yet difficult to exploit. For instance, we present here results on two kind of measurements : the first one consists in a Digital Elevation Model (DEM) built using a correlation algorithm and some optical data, while the second one consists in a DEM obtained by Laser measurments.Our main objective is to design an entirely automatic method that is able to deal with this kind of data in very dense urban areas.We thus focus on elementary shape extraction and propose an algorithm that extracts rectangular buildings. The result provided consists in a kind of vectorial land register map that can be used, for instance, to perform precise roof shape estimation.The proposed algorithm uses our previous work. Using a point process framework, we model towns as configuration of rectangles. An energy is defined, that takes into account both a low level information provided by the altimetry of the scene, and some geometric knowledge of the disposition of buildings in towns.The estimation is done by minimizing the energy using a simulated annealing. We use a MCMC sampler that is a combination of general Metropolis Hastings Green techniques and Geyer and Møller algorithm of sampling of point processes. We use some original proposition kernels, such as birth or death in a neighborhood and define the energy with respect to an inhomogeneous Poisson point process.We present results on real data provided by IGN (French Mapping Institute). Results were automatically obtained, on areas that are 200m by 200m large. These results consist in configurations of around 100 rectangles describing considered areas with an error of 15 missclassification. |
|
9 - Improved RJMCMC Point Process Sampler for Object Detection by Simulated Annealing.
M. Ortner and X. Descombes and J. Zerubia. Research Report 4900, INRIA, France, August 2003.
Keywords : Buildings, Object extraction, RJMCMC, Marked point process.
@TECHREPORT{4900,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Improved RJMCMC Point Process Sampler for Object Detection by Simulated Annealing}, |
| year |
= |
{2003}, |
| month |
= |
{August}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4900}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071683}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71683/filename/RR-4900.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/16/83/PS/RR-4900.ps}, |
| keyword |
= |
{Buildings, Object extraction, RJMCMC, Marked point process} |
| } |
Résumé :
Nous commen ons par résumer l'algorithme de Geyer et Møller qui permet, en utilisant une chaîne de Markov, d'échantillonner des lois de processus ponctuels. Nous rappelons également le cadre théorique proposé par Green qui permet d'imposer la réversibilité d'une chaîne de Markov sous une loi désirée.Dans le cadre de nos applications en traitement d'image, nous sommes intéressés par la simulation de processus ponctuels dont la loi dépend fortement de la localisation géographique des points. Nous présentons donc ici des noyaux de proposition qui améliorent la capacité de l'algorithme de Geyer et Meyer à explorer les bons endroits de l'espace d'état. En particulier, nous proposons une transformation qui permet de faire apparaître ou disparaître des points dans un voisinage quelconque d'un autre point. Nous gardons également la possibilité de générer des points suivant une loi non uniforme.Nous construisons donc de tels noyaux de perturbations grâce au travail de Green de manière à garder la-(.) réversibilité de la chaîne de Markov construite. Nous démontrons ensuite les bonnes propriétés de stabilité qui assurent le bon comportement asymptotique de la chaîne. En particulier, grâce à une condition de «drift», nous montrons l'ergodicité géométrique et la récurrence de la chaîne au sens de Harris.Nous concluons en validant par l'expérience nos résultats théoriques, et en montrons leur utilité sur un exemple concret.Nous proposons d'ultimes améliorations pour conclure. |
Abstract :
We first recall Geyer and Møller algorithm that allows to sample point processes using a Markov chain. We also recall Green's framework that allows to build samplers on general state spaces by imposing reversibility of the designed Markov chain.Since in our image processing applications, we are interested by sampling highly spatially correlated and non-invariant point processes, we adapt these ideas to improve the exploration ability of the algorithm. In particular, we keep the ability of generating points with non-uniform distributions, and design an updating scheme that allows to generate points in some neighborhood of other points. We first design updating schemes under Green's framework to keep (.) reversibility of the Markov chain and then show that stability properties are not loosed. Using a drift condition we prove that the Markov chain is geometrically ergodic and Harris recurrent.We finally show on experimental results that these kinds of updates are usefull and propose other improvements. |
|
10 - Modeling very Oscillating Signals : Application to Image Processing.
G. Aubert and J.F. Aujol. Research Report 4878, INRIA, France, July 2003.
Keywords : Bounded Variation Space, Sobolev space, Image decomposition, Optimization, Partial differential equation.
@TECHREPORT{4878,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Modeling very Oscillating Signals : Application to Image Processing}, |
| year |
= |
{2003}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4878}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071705}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71705/filename/RR-4878.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/17/05/PS/RR-4878.ps}, |
| keyword |
= |
{Bounded Variation Space, Sobolev space, Image decomposition, Optimization, Partial differential equation} |
| } |
Résumé :
Cet article complète le travail présenté dans cite{Aujol[3]} dans lequel nous avions développé l'analyse numérique d'un modéle variationnel, initialement introduit par L. Rudin, S. Osher and E. Fatemi cite{Rudin[1]}, et revisité depuis par Y. Meyer cite{Meyer[1]}, pour supprimer le bruit et isoler les textures dans une image. Dans un tel modèle, on décompose l'image f en deux composantes (u+v), u et v minimisant une énergie. La première composante u appartient à BV et contient l'information géométrique de l'image, alors que la seconde v appartient à un espace G qui contient les signaux à fortes oscillations, i.e. le bruit et les textures. Dans cite{Meyer[1]}, Y. Meyer effectue son étude dans ^2 entier, et son approche repose principalement sur des outils d'analyse harmonique. Nous nous pla ons dans le cas d'un ouvert borné de ^2, ce qui constitue le cadre adapté au traitement d'images, et notre approche repose sur des arguments d'analyse fonctionnelle. Nous définissons l'espace G dans ce cadre puis donnons quelques unes de ses propriétés. Nous étudions ensuite la fonctionnelle permettant de calculer les composantes u et v. |
Abstract :
This article is a companion paper of a previous work cite{Aujol[3]} where we have developed the numerical analysis of a variational model first introduced by L. Rudin, S. Osher and E. Fatemi cite{Rudin[1]} and revisited by Y. Meyer cite{Meyer[1]} for removing the noise and capturing textures in an image. The basic idea in this model is to decompose f into two components (u+v) and then to search for (u,v) as a minimizer of an energy functional. The first component u belongs to BV and contains geometrical informations while the second one v is sought in a space G which contains signals with large oscillations, i.e. noise and textures. In Y. Meyer carried out his study in the whole ^2 and his approach is rather built on harmonic analysis tools. We place ourselves in the case of a bounded set of ^2 which is the proper setting for image processing and our approach is based upon functional analysis arguments. We define in this context the space G, give some of its properties and then study in this continuous setting the energy functional which allows us to recover the components u and v. model signals with strong oscillations. For instance, in an image, this space models noises and textures. case of a bounded open set of ^2 which is the proper setting for image processing. We give a definition of G adapted to our case, and we show that it still has good properties to model signals with strong oscillations. In cite{Meyer[1]}, the author had also paved the way to a new model to decompose an image into two components: one in BV (the space of bounded variations) which contains the geometrical information, and one in G which consists in the noises ad the textures. An algorithm to perform this decomposition has been proposed in cite{Meyer[1]}. We show here its relevance in a continuous setting. |
|
11 - Image Denoising using Stochastic Differential Equations.
X. Descombes and E. Zhizhina. Research Report 4814, INRIA, France, May 2003.
Keywords : Denoising.
@TECHREPORT{4814,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
= |
{Image Denoising using Stochastic Differential Equations}, |
| year |
= |
{2003}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4814}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071772}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71772/filename/RR-4814.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/17/72/PS/RR-4814.ps}, |
| keyword |
= |
{Denoising} |
| } |
Résumé :
Ce rapport concerne le problème de la restauration d'image avec une approche par Équation Différentielle Stochastique. Nous considérons un processus de diffusion convergeant vers une mesure de Gibbs. L'hamiltonien de la mesure de Gibbs contient un terme d'interactions, apportant des contraintes de lissage sur la solution, et un terme d'attache aux données. Nous étudions deux schémas d'approximation discrète de la dynamique de Langevin associée à ce processus de diffusion : les approximation d'Euler et explicite forte de Taylor. La vitesse de convergence des algorithmes correspondants est comparée à celle de l'algorithme de Metropolis-Hasting. Des résultats sont montrés sur des images de synthèse et réelles. Il montrent la supériorité de l'approche proposée lorsque l'on considère un faible nombre d'itérations. |
Abstract :
We address the problem of image denoising using a Stochastic Differential Equation approach. We consider a diffusion process which converges to a Gibbs measure. The Hamiltonian of the Gibbs measure embeds an interaction term, providing smoothing properties, and a data term. We study two discrete approximations of the Langevin dynamics associated with this diffusion process: the Euler and the Explicit Strong Taylor approximations. We compare the convergence speed of the associated algorithms and the Metropolis-Hasting algorithm. Results are shown on synthetic and real data. They show that the proposed approach provides better results when considering a small number of iterations. |
|
12 - The Methodology and Practice of the Evaluation of Image Retrieval Systems and Segmentation Methods.
I. H. Jermyn and C. Shaffrey and N. Kingsbury. Research Report 4761, INRIA, France, March 2003.
Keywords : Image database, Segmentation, Semantic.
@TECHREPORT{4761,
|
| author |
= |
{Jermyn, I. H. and Shaffrey, C. and Kingsbury, N.}, |
| title |
= |
{The Methodology and Practice of the Evaluation of Image Retrieval Systems and Segmentation Methods}, |
| year |
= |
{2003}, |
| month |
= |
{March}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4761}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071825}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71825/filename/RR-4761.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/18/25/PS/RR-4761.ps}, |
| keyword |
= |
{Image database, Segmentation, Semantic} |
| } |
Résumé :
La recherche d'images par le contenu est importante pour deux raisons. Premièrement, la croissance d'archives d'images fréquemment citée dans beaucoup d'applications, et l'expansion rapide du Web, signifient qu'il est nécessaire d'utiliser des systèmes de recherche efficaces pour les bases de données afin que la masse de données accumulée soit utile. Deuxièmement, la recherche dans les bases de données image pose des questions importantes liées à la vision par ordinateur : une recherche efficace demande une véritable compréhension des images. Pour ces raisons, l'évaluation des systèmes de recherche dans les bases de données image devient une priorité. Il existe déjà une littérature importante évaluant des systèmes spécifiques, mais peu de discussions sont publiées sur les méthodes d'évaluation en soi. Dans la première partie de ce rapport, nous proposons un cadre dans lequel ces sujets peuvent être abordés, nous analysons des méthodologies d'évaluation possibles, indiquant quand elles sont pertinentes et quand elles ne le sont pas, et nous critiquons la technique «query-by-example» et les méthodes d'évaluation qui s'y rapportent. Dans la deuxième partie du rapport, nous appliquons les résultats de cette analyse à une collection spécifique d'images. Cette collection est problématique mais typique: il n'existe pas de vérité terrain sémantique. Considérant la recherche fondée sur la segmentation d'image, nous présentons une nouvelle méthode pour son évaluation. Contrairement aux méthodes d'évaluation qui reposent sur l'existence ou la création d'une vérité terrain, la méthodologie proposée utilise des sujets humains pour un test psychovisuel qui compare les résultats des différentes méthodes de segmentation. Le test est con u pour répondre à deux questions : existe-t-il une segmentation «meilleure» que les autres et si oui qu'apprenons-nous des méthodes de segmentation pour la recherche dans des bases de données image? Les résultats confirment la cohérence des jugements humains, permettant ainsi une évaluation significative. |
Abstract :
Content-Based Image Retrieval is important for two reasons. First, the oft-cited growth of image archives in many fields, and the rapid expansion of the Web, mean that successful image retrieval systems are fast becoming a necessity if the mass of accumulated data is to be useful. Second, database retrieval provides a framework within which the important questions of machine vision are brought into focus: successful retrieval is likely to require genuine image understanding. In view of these points, the evaluatio- n of retrieval systems becomes a matter of priority. There is already a substantial literature evaluating specific systems, but little high-level discussion of the evaluation methodologies themselves seems to have taken place. In the first part of the report, we propose a framework within which such issues can be addressed, analyse possible evaluation methodologies, indicate where they are appropriate and where they are not, and critique query-by-example and evaluation methodologies related to it. In the second part of the report, we apply the results of this analysis to a particular dataset. The dataset is problematic but typical: no ground truth is available for its semantics. Considering retrieval based on image segmentation- s, we present a novel method for its evaluation. Unlike methods of evaluation that rely on the existence or creation of ground truth, the proposed evaluatio- n procedure subjects human subjects to a psychovisual test comparing the results of different segmentation schemes. The test is designed to answer two questions: does consensus about a `best' segmentation exist, and if it does, what do we learn about segmentation schemes for retrieval? The results confirm that human subjects are consistent in their judgements, thus allowing meaningful evaluation. |
|
13 - Image Decomposition : Application to Textured Images and SAR Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. Research Report 4704, INRIA, France, January 2003.
Keywords : Total variation, Bounded Variation Space, Texture, Classification, Restoration, Synthetic Aperture Radar (SAR).
@TECHREPORT{4704,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Image Decomposition : Application to Textured Images and SAR Images}, |
| year |
= |
{2003}, |
| month |
= |
{January}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4704}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071882}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71882/filename/RR-4704.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/18/82/PS/RR-4704.ps}, |
| keyword |
= |
{Total variation, Bounded Variation Space, Texture, Classification, Restoration, Synthetic Aperture Radar (SAR)} |
| } |
Résumé :
Dans ce rapport, nous présentons un nouvel algorithme pour décomposer une imagef en u+v, u étant à variation bornée, et v contenant les textures et le bruit de l'image originale. Nous introduisons une fonctionnelle adaptée à ce problème. Le minimum de cette fonctionnelle correspond à la décomposition cherchée de l'image. Le calcul de ce minimum se fait par minimisation successive par rapport à chacune des variables, chaque minimisati- on étant réalisée à l'aide d'un algorithme de projection. Nous faisons l'étude théorique de notre modèle, et nous présentons des résultats numériques. D'une part, nous montrons comment la composante v peut être utilisée pour faire de la classification d'images texturées, et d'autre part nous montrons comment la composante u peut être utilisée en restauration d'images SAR. |
Abstract :
In this report, we present a new algorithm to split an image f into a component u belonging to BV and a component v made of textures and noise of the initial image. We introduce a functional adapted to this problem. The minimum of this functional corresponds to the image decomposition we want to get. We compute this minimum by minimizing successively our functional with respect to u and v. We carry out the mathematical study of our algorithm. We present some numerical results. On the one hand, we show how the v component can be used to classify textured images, and on the other hand, we show how the u component can be used in SAR image restoration. |
|
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Book |
1 - Energy Minimization Methods in Computer Vision and Pattern Recognition.
A. Rangarajan and M. Figueiredo and J. Zerubia. Publ. Springer Verlag, (LNCS 2683), July 2003.
@BOOK{Rangarajan03,
|
| author |
= |
{Rangarajan, A. and Figueiredo, M. and Zerubia, J.}, |
| title |
= |
{Energy Minimization Methods in Computer Vision and Pattern Recognition}, |
| year |
= |
{2003}, |
| month |
= |
{July}, |
| publisher |
= |
{Springer Verlag}, |
| number |
= |
{LNCS 2683}, |
| url |
= |
{http://www.springer.com/us/book/9783540404989}, |
| keyword |
= |
{} |
| } |
|
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