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|
Publications of 2001
Result of the query in the list of publications :
2 Articles |
1 - Globally optimal regions and boundaries as minimum ratio weight cycles.
I. H. Jermyn and H. Ishikawa. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(10): pages 1075-1088, October 2001.
Keywords : Graph, Ratio, Cycle, Segmentation, Global minimum.
Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@ARTICLE{jermyn_tpami01,
|
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{Jermyn, I. H. and Ishikawa, H.}, |
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{Globally optimal regions and boundaries as minimum ratio weight cycles}, |
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{2001}, |
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{October}, |
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{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
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{1075-1088}, |
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{Graph, Ratio, Cycle, Segmentation, Global minimum} |
| } |
Abstract :
We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,ldots), em and from the interior of the region (texture, homogeneity,ldots). We describe two polynomial-time digraph algorithms for finding the em global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 times 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization. |
|
2 - A RJMCMC algorithm for object processes in image processing.
X. Descombes and R. Stoica and L. Garcin and J. Zerubia. Monte Carlo Methods and Applications, 7(1-2): pages 149-156, 2001.
@ARTICLE{xd01c,
|
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{Descombes, X. and Stoica, R. and Garcin, L. and Zerubia, J.}, |
| title |
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{A RJMCMC algorithm for object processes in image processing}, |
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{Monte Carlo Methods and Applications}, |
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{149-156}, |
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{} |
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|
 top of the page
2 PhD Thesis and Habilitations |
1 - Modèles, estimation bayésienne et algorithmes pour la déconvolution d'images satellitaires et aériennes.
A. Jalobeanu. PhD Thesis, Universite de Nice Sophia Antipolis, December 2001.
@PHDTHESIS{aj01,
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2 - Processus ponctuels pour l'extraction de réseaux linéiques dans les images satellitaires et aériennes.
R. Stoica. PhD Thesis, Universite de Nice Sophia Antipolis, February 2001.
Keywords : Marked point process, Line networks, Road network, Stochastic geometry, RJMCMC.
@PHDTHESIS{rs01,
|
| author |
= |
{Stoica, R.}, |
| title |
= |
{Processus ponctuels pour l'extraction de réseaux linéiques dans les images satellitaires et aériennes}, |
| year |
= |
{2001}, |
| month |
= |
{February}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{Theses/These-stoica.pdf}, |
| keyword |
= |
{Marked point process, Line networks, Road network, Stochastic geometry, RJMCMC} |
| } |
Résumé :
Les réseaux routiers, ou les réseaux hydrographiques, les vaisseaux sanguins ou bien les fissures dans les matériaux sont connus dans la communauté du traitement d'image sous le nom générique de réseaux liné¨iques. La théorie des processus ponctuels marqués est un cadre mathématique rigoureux qui donne la possibilité de modéliser l'image comme un ensemble d'objets en interaction. Les deux idées principales qui ont motivé ce travail sont : ces réseaux sont approchés par de segments de droite connectés, et les réseaux liné¨iques dans une image sont la réalisation d'un processus ponctuel de Gibbs. Le processus ponctuel qui modèlise les réseaux comporte deux composantes. Le premier terme ("Candy" modèle) gère les états et les interactions entre segments : densité, connectivité, alignement et répulsion des segments. L'emplacement du réseau dans l'image est trouvé grâce au second terme, le terme d'attache aux données. Cette composante du modèle est construite à partir de tests d'hypothèses. L'estimateur des réseaux dans l'image est donné par le minimum d'une fonction d'énergie de Gibbs. Pour trouver l'optimum global de cette fonction, nous mettons en {\oe}uvre un algorithme de type recuit simulé qui s'appuie, sur une dynamique de type Monte Carlo par Chaînes de Markov (MCMC) à sauts réversibles. Des résultats sont présentes sur des images aériennes, SPOT et RADAR (RSO). Nous abordons ensuite deux de problèmes ouverts liés au "Candy" modèle, mais d'un interêt théorique général : la convergence d'une dynamique de Monte Carlo à sauts reversibles, et l'estimation des paramètres des processus ponctuels. Une solution à ces problèmes pourrait ouvrir une nouvelle direction dans la recherche de méthodes non-supervisése en traitement d'image. |
Abstract :
Road or hydrographical networks, blood vessels or fissures in materials are all known by the image processing community under the general name of line networks. The theory of point processes is a rigourous mathematical framework which allows us to model an image as a set of interacting objects. The two main ideas which are the basis of this work are : these networks can be considered as connected segments, and the line networks in an image are the realization of a Gibbs point process. The point process used to model the networks has two components. The first one (Candy model) deals with the states and the interaction of the segments : density, connectivity, alignment, attraction and rejection. The location of the network is determined by the second component, the data term. This component is based on hypothesis tests. The network estimator is given by the minimum of a Gibbs energy. We build a simulated annealing algorithm in order to avoid local minima. This algorithm uses reversible jump Monte Carlo Markov Chain (RJMCMC) dynamics. Results are shown on aerial, SPOT and RADAR (SAR) images. Finally, we start a study on two open problems related to the Candy model, but of general theoretical interest : the convergence of a RJMCMC dynamics, and parameter estimation related to point processes. A solution to these problems would give a new direction for the research of unsupervised methods in image processing. |
|
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15 Conference articles |
1 - Building extraction using a Markov point process.
L. Garcin and X. Descombes and J. Zerubia and H. Le Men. In Proc. IEEE International Conference on Image Processing (ICIP), papier invité, Thessalonique, Grèce, October 2001.
@INPROCEEDINGS{xd01d,
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{Garcin, L. and Descombes, X. and Zerubia, J. and Le Men, H.}, |
| title |
= |
{Building extraction using a Markov point process}, |
| year |
= |
{2001}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{papier invité, Thessalonique, Grèce}, |
| url |
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{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=958555}, |
| keyword |
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{} |
| } |
|
2 - Image deconvolution using Hidden Markov Tree modeling of complex wavelet packets.
A. Jalobeanu and N. Kingsbury and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Thessalonique, Grèce, October 2001.
@INPROCEEDINGS{aj01b,
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| author |
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{Jalobeanu, A. and Kingsbury, N. and Zerubia, J.}, |
| title |
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{Image deconvolution using Hidden Markov Tree modeling of complex wavelet packets}, |
| year |
= |
{2001}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Thessalonique, Grèce}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=958988}, |
| keyword |
= |
{} |
| } |
|
3 - Two variational models for multispectral image classification.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Sophia Antipolis, France, September 2001.
@INPROCEEDINGS{lbf01a,
|
| author |
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{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
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{Two variational models for multispectral image classification}, |
| year |
= |
{2001}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{Sophia Antipolis, France}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-44745-8_23}, |
| keyword |
= |
{} |
| } |
|
4 - Classification d'image satellitaire superspectrale en zone rurale et périurbaine.
O. Pony and U. Polverini and L. Gautret and J. Zerubia and X. Descombes. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001.
@INPROCEEDINGS{xd01f,
|
| author |
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{Pony, O. and Polverini, U. and Gautret, L. and Zerubia, J. and Descombes, X.}, |
| title |
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{Classification d'image satellitaire superspectrale en zone rurale et périurbaine}, |
| year |
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{2001}, |
| month |
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{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
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= |
{Toulouse, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13236}, |
| keyword |
= |
{} |
| } |
|
5 - Un modèle markovien gaussien pour l'analyse de texture hyperspectrale en milieu urbain.
G. Rellier and X. Descombes and J. Zerubia and F. Falzon. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001.
@INPROCEEDINGS{xd01g,
|
| author |
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{Rellier, G. and Descombes, X. and Zerubia, J. and Falzon, F.}, |
| title |
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{Un modèle markovien gaussien pour l'analyse de texture hyperspectrale en milieu urbain}, |
| year |
= |
{2001}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13293}, |
| keyword |
= |
{} |
| } |
|
6 - Recuit simulé pour le shape from shading.
X. Descombes and J.D. Durou and L. Petit. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001.
@INPROCEEDINGS{xd01h,
|
| author |
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{Descombes, X. and Durou, J.D. and Petit, L.}, |
| title |
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{Recuit simulé pour le shape from shading}, |
| year |
= |
{2001}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13322}, |
| keyword |
= |
{} |
| } |
|
7 - Estimation de paramètres instrumentaux en imagerie satellitaire.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Toulouse, France, September 2001.
@INPROCEEDINGS{aj01d,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
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{Estimation de paramètres instrumentaux en imagerie satellitaire}, |
| year |
= |
{2001}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/13231}, |
| keyword |
= |
{} |
| } |
|
8 - Parameter estimation by a Markov Chain Monte Carlo technique for the Candy-model.
X. Descombes and M.N.M. van Lieshout and R. Stoica and J. Zerubia. In IEEE Workshop on Statistical Signal Processing, papier invité, Singapour, August 2001.
@INPROCEEDINGS{xd01e,
|
| author |
= |
{Descombes, X. and van Lieshout, M.N.M. and Stoica, R. and Zerubia, J.}, |
| title |
= |
{Parameter estimation by a Markov Chain Monte Carlo technique for the Candy-model}, |
| year |
= |
{2001}, |
| month |
= |
{August}, |
| booktitle |
= |
{IEEE Workshop on Statistical Signal Processing}, |
| address |
= |
{papier invité, Singapour}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=955212}, |
| keyword |
= |
{} |
| } |
|
9 - Region extraction from multiple images.
H. Ishikawa and I. H. Jermyn. In Proc. IEEE International Conference on Computer Vision (ICCV), Vancouver, Canada, July 2001.
Keywords : Stereo, Motion, global, optimum, Graph, Cycle.
@INPROCEEDINGS{IJ01a,
|
| author |
= |
{Ishikawa, H. and Jermyn, I. H.}, |
| title |
= |
{Region extraction from multiple images}, |
| year |
= |
{2001}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Vancouver, Canada}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jermyn01iccv.pdf}, |
| keyword |
= |
{Stereo, Motion, global, optimum, Graph, Cycle} |
| } |
Abstract :
We present a method for region identification in multiple
images. A set of regions in different images and the
correspondences on their boundaries can be thought of as
a boundary in the multi-dimensional space formed by the
product of the individual image domains. We minimize an
energy functional on the space of such boundaries, thereby
identifying simultaneously both the optimal regions in each
image and the optimal correspondences on their boundaries.
We use a ratio form for the energy functional, thus
enabling the global minimization of the energy functional
using a polynomial time graph algorithm, among other desirable
properties. We choose a simple form for this energy
that favours boundaries that lie on high intensity gradients
in each image, while encouraging correspondences between
boundaries in different images that match intensity values.
The latter tendency is weighted by a novel heuristic energy
that encourages the boundaries to lie on disparity or optical
flow discontinuities, although no dense optical flow or
disparity map is computed. |
|
10 - La poursuite de projection pour la classification d'images hyperspectrales texturées.
G. Rellier and X. Descombes and J. Zerubia and F. Falzon. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Cahors, France, June 2001.
@INPROCEEDINGS{xd01i,
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| author |
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{Rellier, G. and Descombes, X. and Zerubia, J. and Falzon, F.}, |
| title |
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{La poursuite de projection pour la classification d'images hyperspectrales texturées}, |
| year |
= |
{2001}, |
| month |
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{June}, |
| booktitle |
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{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
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| url |
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| keyword |
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{} |
| } |
|
11 - Apport de l'imagerie radar pour l'extraction des zones urbaines.
O. Viveros-Cancino and X. Descombes and J. Zerubia. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Cahors, France, June 2001.
@INPROCEEDINGS{xd01j,
|
| author |
= |
{Viveros-Cancino, O. and Descombes, X. and Zerubia, J.}, |
| title |
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{Apport de l'imagerie radar pour l'extraction des zones urbaines}, |
| year |
= |
{2001}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Cahors, France}, |
| url |
= |
{http://www.irit.fr/ORASIS2001/}, |
| ps |
= |
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| keyword |
= |
{} |
| } |
|
12 - Estimation rapide du paramètre de régularisation en déconvolution d'images.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Cahors, France, June 2001.
@INPROCEEDINGS{aj01c,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Estimation rapide du paramètre de régularisation en déconvolution d'images}, |
| year |
= |
{2001}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Cahors, France}, |
| url |
= |
{http://www.irit.fr/ORASIS2001/}, |
| ps |
= |
{http://www.irit.fr/ORASIS2001/images/docs/jalobeanu.ps.gz}, |
| keyword |
= |
{} |
| } |
|
13 - Judging whether multiple silhouettes can come from the same object.
D. Jacobs and P. Belhumeur and I. H. Jermyn. In Int. Workshop on Visual Form, Springer-Verlag Lecture Notes in Computer Science 2059, Capri, Italie, May 2001.
@INPROCEEDINGS{IJ01b,
|
| author |
= |
{Jacobs, D. and Belhumeur, P. and Jermyn, I. H.}, |
| title |
= |
{Judging whether multiple silhouettes can come from the same object}, |
| year |
= |
{2001}, |
| month |
= |
{May}, |
| booktitle |
= |
{Int. Workshop on Visual Form, Springer-Verlag Lecture Notes in Computer Science 2059}, |
| address |
= |
{Capri, Italie}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Jacobs01iwvf.pdf}, |
| keyword |
= |
{} |
| } |
Abstract :
We consider the problem of recognizing an object from its
silhouette. We focus on the case in which the camera translates, and
rotates about a known axis parallel to the image, such as when a mo-
bile robot explores an environment. In this case we present an algorithm
for determining whether a new silhouette could come from the same ob-
ject that produced two previously seen silhouettes. In a basic case, when
cross-sections of each silhouette are single line segments, we can check
for consistency between three silhouettes using linear programming. This
provides the basis for methods that handle more complex cases. We show
many experiments that demonstrate the performance of these methods
when there is noise, some deviation from the assumptions of the algo-
rithms, and partial occlusion. Previous work has addressed the problem
of precisely reconstructing an object using many silhouettes taken under
controlled conditions. Our work shows that recognition can be performed
without complete reconstruction, so that a small number of images can
be used, with viewpoints that are only partly constrained. |
|
14 - Modelling images with alpha-stable textures.
E.E. Kuruoglu and J. Zerubia. In Physics in Signal and Image Processing, Marseille, France, January 2001.
@INPROCEEDINGS{KuruJZ01,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling images with alpha-stable textures}, |
| year |
= |
{2001}, |
| month |
= |
{January}, |
| booktitle |
= |
{Physics in Signal and Image Processing}, |
| address |
= |
{Marseille, France}, |
| keyword |
= |
{} |
| } |
|
15 - Segmentation d'image haute résolution par processus Markov objet.
X. Descombes and S. Drot and M. Imberty and H. Le Men and J. Zerubia. In Séminaire Télédétection à très haute résolution spatiale et analyse d'image, Cemagref, Montpellier, France, 2001.
@INPROCEEDINGS{xd01k,
|
| author |
= |
{Descombes, X. and Drot, S. and Imberty, M. and Le Men, H. and Zerubia, J.}, |
| title |
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{Segmentation d'image haute résolution par processus Markov objet}, |
| year |
= |
{2001}, |
| booktitle |
= |
{Séminaire Télédétection à très haute résolution spatiale et analyse d'image, Cemagref}, |
| address |
= |
{Montpellier, France}, |
| url |
= |
{http://cemadoc.irstea.fr/oa/PUB00009549-segmentation-image-haute-resolution-par-processus.html}, |
| keyword |
= |
{} |
| } |
|
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4 Technical and Research Reports |
1 - Segmentation of textured satellite and aerial images by Bayesian inference and Markov Random Fields.
S. Wilson and J. Zerubia. Research Report 4336, INRIA, France, December 2001.
@TECHREPORT{wilsonJZ01,
|
| author |
= |
{Wilson, S. and Zerubia, J.}, |
| title |
= |
{Segmentation of textured satellite and aerial images by Bayesian inference and Markov Random Fields}, |
| year |
= |
{2001}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{4336}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00072251}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72251/filename/RR-4336.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/22/51/PS/RR-4336.ps}, |
| keyword |
= |
{} |
| } |
Résumé :
Nous étudions un modèle markovien double, initialement proposé par Melas et Wilson, pour la segmentation d'image. Le nombre de classes de l'image est obtenu par inférence bayésienne via un algorithme de Metropolis à saut réversible. Les mouvements habituellement utilisés dans une telle dynamique consistent en la fission ou la fusion de classes. Mais cela peut nécessiter beaucoup de temps de calcul, en particulier sur des images de grande taille. Ici, nous étudions des mouvements plus simples qui sont rapides à mettre en oeuvre, mais dont la mélangeance peut être longue. Nous proposons alors un schéma de fission/fusion plus complexe et comparons les performances obtenues. Nous effectuons des tests sur des images satellitai- res et aériennes. |
Abstract :
We investigate Bayesian solutions to image segmentation based on the double Markov random field model, originally proposed by Melas and Wilson. Inference on the number of classes in the image is done via reversible jump Metropolis moves. These moves, usually implemented by splitting and merging classes, can be very slow, making them impractical for large images. We investigate simpler reversible jump moves that are quick to implement but show that they may mix very slowly. We propose a more complex split and merge scheme and compare its performance. Tests are conducted on satellite and aerial images. |
|
2 - Building detection by markov object processes and a MCMC algorithm.
L. Garcin and X. Descombes and J. Zerubia and H. Le Men. Research Report 4206, Inria, France, June 2001.
Keywords : Stochastic geometry, Marked point process, Buildings, RJMCMC.
@TECHREPORT{xd01a,
|
| author |
= |
{Garcin, L. and Descombes, X. and Zerubia, J. and Le Men, H.}, |
| title |
= |
{Building detection by markov object processes and a MCMC algorithm}, |
| year |
= |
{2001}, |
| month |
= |
{June}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{4206}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00072416}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72416/filename/RR-4206.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/24/16/PS/RR-4206.ps}, |
| keyword |
= |
{Stochastic geometry, Marked point process, Buildings, RJMCMC} |
| } |
Résumé :
Le but de ce travail est de détecter les bâtiments à partir de photographies aeriennes numériques. Nous modélisons un ensemble de bâtiments par une configuration d'objets. Nous définissons un processus ponctuel sur l'ensemble des configurations qui se décompose en deux parties :
* La première est un modèle a priori sur les configurations qui considère des interactions entre les objets,
* la seconde est un modèle d'attache aux données qui induit la cohérence du résultat avec l'image traitée.
Nous avons ainsi une distribution a posteriori dont nous recherchons la configuration maximale. Pour obtenir ce maximum, nous utilisons une simulatio- n de type MCMC - un algorithme de Metropolis-Hasting-Green- couplée avec un schéma de recuit simulé. Nous testons la méthode décrite à la fois sur des données synthétiques et des images stéréoscopiques réelles. |
Abstract :
This work aims at detecting buildings in digital aerial photographs. Here we model a set of buildings by a configuration of objects. We define a point process on the set of configurations, which splits into two parts :
* the first one is a prior model on the configurations which use interactions between objects,
* the second one is a data model which enforces the coherence with the image.
Thus we have a posterior distribution whose maximum has to be found. In order to achieve this maximum, we use a MCMC simulation - a Metropolis-Hasting- s-Green algorithm - mixed with a simulated annealing. Then we test this method on both synthetic and real stereo-images. |
|
3 - La poursuite de projection pour la classification d'image hyperspectrale texturée.
G. Rellier and X. Descombes and F. Falzon and J. Zerubia. Research Report 4152, Inria, France, March 2001.
Keywords : Classification, Texture, Hyperspectral imaging, Markov Fields.
@TECHREPORT{xd01,
|
| author |
= |
{Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, |
| title |
= |
{La poursuite de projection pour la classification d'image hyperspectrale texturée}, |
| year |
= |
{2001}, |
| month |
= |
{March}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{4152}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00072472}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72472/filename/RR-4152.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/24/72/PS/RR-4152.ps}, |
| keyword |
= |
{Classification, Texture, Hyperspectral imaging, Markov Fields} |
| } |
Résumé :
Dans ce travail, nous considérons le problème de la classification supervisée de texture à partir d'images multi-composante de télédetection, dites hyperspectrales. Ces images, le plus souvent acquises par des instruments spectro-imageurs dont le nombre de canaux est en général supérieur à 10, fournissent ainsi une représentation du paysage échantillonnée à la fois spatialement et spectralement. Le but de ce travail est de réaliser une analyse de texture qui se déroule conjointement dans ces deux espaces discrets. On recherche ainsi à enrichir la représentation "habituelle" de texture fondée sur la prise en compte des variations locales de contraste, par l'adjonction d'une connaissance sur ses variations spectrales. L'applicati- on qui est susceptible de bénéficier directement des résultats de cette étude est la classification du tissu urbain. En effet, la réponse spectrale (radiométrique) des zones urbaines est en général ambiguë du fait de la similitude de réponse spectrale de certains matériaux constitutifs du paysage urbain avec certains éléments naturels tels que l'eau, le sol nu, la végétation. La multiplication des bandes spectrales a pour conséquence de rendre plus complexes les mesures et demande également la prise en considération d'un nombre d'échantillons d'apprentissage très important. Quand le nombre de ces échantillons n'est pas suffisant, il faut passer par une étape de réduction de la dimension de l'espace d'observation. Pour prendre en compte le problème de la dimension et celui de l'analyse de texture conjointement dans le domaine spatial et spectral, on se propose ici de faire coopérer un algorithme de poursuite de projection paramétrique, déjà utilisé pour la réduction d'espace dans un cadre non-contextuel, à un modèle de texture par champ markovien, dit modèle markovien gaussien. |
Abstract :
In this work we develop a supervised texture classification algorithm for application to the class of multi-component images called hyperspectral. These images, usually recorded by spectrometers with a number of bands greater than 10, give both a spatially and spectrally sampled representation of a remote scene. The aim of this work is to perform a joint texture analysis in both discrete spaces. The use of spectral variations in this joint texture analysis scheme enables us to improve on the standard representa- tion of textures which only takes into account the local contrast variations. A likely application of this work is urban area classification. Indeed, the spectral response of urban areas is in general ambiguous because some of its constitutive elements have the same reflectance as natural elements such as water, vegetation or bare soil. The greater number of spectral bands makes the measures more complex and so creates the need for a greater number of training samples. When the number of training samples is not sufficient, a necessary step in the analysis is to reduce the dimension of the observation space. To take into account both the problem of dimensional- ity and the jointly spectral and spatial texture analysis, we propose to use in cooperation a projection pursuit algorithm and a Gauss-Markov random field texture model. |
|
4 - Modelling SAR images with a generalization of the Rayleigh distribution.
E.E. Kuruoglu and J. Zerubia. Research Report 4121, Inria, France, February 2001.
Keywords : Alpha-stable distribution.
@TECHREPORT{KuruJZ01a,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling SAR images with a generalization of the Rayleigh distribution}, |
| year |
= |
{2001}, |
| month |
= |
{February}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{4121}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00072507}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72507/filename/RR-4121.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/25/07/PS/RR-4121.ps}, |
| keyword |
= |
{Alpha-stable distribution} |
| } |
Résumé :
L'imagerie Radar à Synthése d'Ouverture (RSO) a conduit à d'importantes applications, du fait de son avantage certain sur l'imagerie satellitaire optique (utilisation tout temps).Cependant, du fait de la physique du capteur RSO, les images produites présentent des artefacts non désirables, connus sous le nom de bruit de chatoiement. L'hypothèse que les parties réelles et qqimaginaires del'onde reçue suivent une loi Gaussienne (ce qui revient à dire que l'amplitude de l'onde suit une distribution de Rayleigh)découle des hypothèses classiquement faites sur le modèle de génération de l'image RSO.
Cependant, des données expérimentales présentent des charactéristiques impulsionnelles correspondant à des distributions à queue lourde sous-jascente- s, qui ne sont pas de type Rayleigh. D'autres distributions telles que les lois de Weibull ou log-normale ont été proposées. Cependant, dans la plupart des cas, ces modèles sont empiriques ne prenant pas, encompte la physique du capteur, et sont trop spécifiques.
Dans ce rapport, en relachant quelques hypothèses qui conduisent au modèle de Rayleigh et en utilisant des résultats récents publiés dans la littérature surles distributions $alpha$-stables, nous proposons une version généralisée (à queue lourde) du modèle de Rayleigh. Ceci est fondé sur l'hypothèse que les parties reélle et imaginaire du signal reçu suivent une loi $alpha$-s- table isotrope, suggérée par une généralisation du théorème central limite. Nous présentons également de nouvelles mèthodes d'estimation des paramètres d'une distribution de Rayleigh à queue lourde fondées sur des statistiques d'ordre fractionnaire négatif. Les tests expérimentaux montrent que le modèle de Rayleigh à queue lourde permet de décrire une grande variété de données qui ne pourraient pas être décrites defaçon satisfaisante par un modèle de Rayleigh classique. |
Abstract :
Synthetic aperture radar (SAR) imagery has found important applications since its introduction, due to its clear advantage over optical satellite imagery, being operable in various weather conditions. However, due to the physics of radar imaging process, sar images contain unwanted artefacts in the form of a granular look which is called speckle. the assumptions of the classical SAR image generation model lead to the convention that the real and imaginary parts of the received wave follow a Gaussian law, which in turn means that the amplitude of the wave has a Rayleigh distribution- . However, some experimental data show impulsive characteristics which correspond to underlying heavy-tailed distributions, clearly non-rayleigh. some alternative distributions have been suggested such as weibull and log-normal distributions, however, in most of the cases these models are empirical, not derived with the consideration of underlying physical conditions and therefore are case specific. In this report, relaxing some of the assumptions leading to the classical rayleigh model and using the recent results in the literature on $alpha$-stable distributions, we develop a generalised (heavy-tailed) version of the rayleigh model based on the assumption that the real and the imaginary parts of the received signal follows an isotropic $alpha$-stable law which is suggested by a generalised form of the central limit theorem. we also derive novel methods for the estimation of the heavy-tailed rayleigh distribution parameter- s based on negative fractional-order statistics for model fitting. our experimental results show that the heavy-tailed rayleigh model can describe a wide range of data which could not be described by the classical rayleigh model. |
|
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2 Collection articles or Books chapters |
1 - Problèmes non supervisés.
X. Descombes and Y. Goussard. In Approche bayésienne pour les problèmes inverses, Ed. J. Idier, Publ. Hermes, 2001.
Copyright :
@INCOLLECTION{GoussardXD01,
|
| author |
= |
{Descombes, X. and Goussard, Y.}, |
| title |
= |
{Problèmes non supervisés}, |
| year |
= |
{2001}, |
| booktitle |
= |
{Approche bayésienne pour les problèmes inverses}, |
| editor |
= |
{J. Idier}, |
| publisher |
= |
{Hermes}, |
| url |
= |
{http://editions.lavoisier.fr/mathematiques/approche-bayesienne-pour-les-problemes-inverses/idier/hermes-science-publications/traite-ic2/livre/9782746203488}, |
| keyword |
= |
{} |
| } |
|
2 - Déconvolution en imagerie.
J. Idier and L. Blanc-Féraud. In Approche bayésienne pour les problèmes inverses, Ed. J. Idier, Publ. Hermes, 2001.
@INCOLLECTION{lbflivre,
|
| author |
= |
{Idier, J. and Blanc-Féraud, L.}, |
| title |
= |
{Déconvolution en imagerie}, |
| year |
= |
{2001}, |
| booktitle |
= |
{Approche bayésienne pour les problèmes inverses}, |
| editor |
= |
{J. Idier}, |
| publisher |
= |
{Hermes}, |
| url |
= |
{http://editions.lavoisier.fr/mathematiques/approche-bayesienne-pour-les-problemes-inverses/idier/hermes-science-publications/traite-ic2/livre/9782746203488}, |
| keyword |
= |
{} |
| } |
|
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Book |
1 - Energy minimization methods in computer vision and pattern recognition.
M. Figueiredo and J. Zerubia and A.K. Jain. Publ. Springer Verlag, (LNCS 2134), 2001.
@BOOK{JZ,
|
| author |
= |
{Figueiredo, M. and Zerubia, J. and Jain, A.K.}, |
| title |
= |
{Energy minimization methods in computer vision and pattern recognition}, |
| year |
= |
{2001}, |
| publisher |
= |
{Springer Verlag}, |
| number |
= |
{LNCS 2134}, |
| url |
= |
{http://www.springer.com/us/book/9783540425236}, |
| keyword |
= |
{} |
| } |
|
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