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Publications de 2005
Résultat de la recherche dans la liste des publications :
9 Articles |
1 - Detecting codimension-two objects in an image with Ginzburg-Landau models.
G. Aubert et J.F. Aujol et L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, novembre 2005.
Mots-clés : Modele de Ginzburg-Landau, Detection de points, Segmentation, PDE, Images biologiques, Images SAR.
@ARTICLE{laure-ijcv05,
|
| author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
| title |
= |
{Detecting codimension-two objects in an image with Ginzburg-Landau models}, |
| year |
= |
{2005}, |
| month |
= |
{novembre}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{65}, |
| number |
= |
{1-2}, |
| pages |
= |
{29-42}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, |
| keyword |
= |
{Modele de Ginzburg-Landau, Detection de points, Segmentation, PDE, Images biologiques, Images SAR} |
| } |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from
Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its
mathematical properties and give some experimental results demonstrating its capability in image processing. |
|
2 - Point Processes for Unsupervised Line Network Extraction in Remote Sensing.
C. Lacoste et X. Descombes et J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, octobre 2005.
@ARTICLE{lacoste05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point Processes for Unsupervised Line Network Extraction in Remote Sensing}, |
| year |
= |
{2005}, |
| month |
= |
{octobre}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{27}, |
| number |
= |
{10}, |
| pages |
= |
{1568-1579}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=32189&arnumber=1498752&count=18&index=4}, |
| keyword |
= |
{} |
| } |
|
3 - Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model.
G. Poggi et G. Scarpa et J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 43(8): pages 1901-1911, août 2005.
Mots-clés : Classification, Segmentation, Champs de Markov.
@ARTICLE{ieeetgrs_05,
|
| author |
= |
{Poggi, G. and Scarpa, G. and Zerubia, J.}, |
| title |
= |
{Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model}, |
| year |
= |
{2005}, |
| month |
= |
{août}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{43}, |
| number |
= |
{8}, |
| pages |
= |
{1901-1911}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/32001/01487647.pdf?tp=&arnumber=1487647&isnumber=32001}, |
| keyword |
= |
{Classification, Segmentation, Champs de Markov} |
| } |
|
4 - Dual Norms and Image Decomposition Models.
J.F. Aujol et A. Chambolle. International Journal of Computer Vision, 63(1): pages 85-104, juin 2005.
Mots-clés : Decomposition d'images.
@ARTICLE{AujolChambolle,
|
| author |
= |
{Aujol, J.F. and Chambolle, A.}, |
| title |
= |
{Dual Norms and Image Decomposition Models}, |
| year |
= |
{2005}, |
| month |
= |
{juin}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{63}, |
| number |
= |
{1}, |
| pages |
= |
{85-104}, |
| pdf |
= |
{http://link.springer.com/article/10.1007/s11263-005-4948-3}, |
| keyword |
= |
{Decomposition d'images} |
| } |
|
5 - Invariant Bayesian estimation on manifolds.
I. H. Jermyn. Annals of Statistics, 33(2): pages 583--605, avril 2005.
Mots-clés : Estimation bayesienne, MAP, MMSE, Invariant, Metrique, Jeffrey's.
@ARTICLE{jermyn_annstat05,
|
| author |
= |
{Jermyn, I. H.}, |
| title |
= |
{Invariant Bayesian estimation on manifolds}, |
| year |
= |
{2005}, |
| month |
= |
{avril}, |
| journal |
= |
{Annals of Statistics}, |
| volume |
= |
{33}, |
| number |
= |
{2}, |
| pages |
= |
{583--605}, |
| url |
= |
{http://dx.doi.org/10.1214/009053604000001273}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_annstat05.pdf}, |
| keyword |
= |
{Estimation bayesienne, MAP, MMSE, Invariant, Metrique, Jeffrey's} |
| } |
Abstract :
A frequent and well-founded criticism of the maximum em a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter param taking values in a differentiable manifold paramspace is that they are not invariant to arbitrary `reparametrizations' of paramspace. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a em sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and then provides a solution. We first show that the presence of a metric structure on paramspace can be used to define coordinate-invariant MAP and MMSE estimates, and we argue that this is the natural way to proceed. We then discuss the choice of a metric structure on paramspace. By imposing an invariance criterion natural within a Bayesian framework, we show that this choice is essentially unique. It does not necessarily correspond to a choice of coordinates. In cases of complete prior ignorance, when Jeffreys' prior is used, the invariant MAP estimate reduces to the maximum likelihood estimate. The invariant MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation. |
|
6 - Modeling very Oscillating Signals. Application to Image Processing.
G. Aubert et J.F. Aujol. Applied Mathematics and Optimization, 51(2): pages 163--182, mars 2005.
@ARTICLE{AujolAubert,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Modeling very Oscillating Signals. Application to Image Processing}, |
| year |
= |
{2005}, |
| month |
= |
{mars}, |
| journal |
= |
{Applied Mathematics and Optimization}, |
| volume |
= |
{51}, |
| number |
= |
{2}, |
| pages |
= |
{163--182}, |
| pdf |
= |
{http://link.springer.com/article/10.1007/s00245-004-0812-z}, |
| keyword |
= |
{} |
| } |
|
7 - Optimal Partitions, Regularized Solutions, and Application to Image Classification.
G. Aubert et J.F. Aujol. Applicable Analysis, 84(1): pages 15--35, janvier 2005.
@ARTICLE{AujolAubertclassif,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Optimal Partitions, Regularized Solutions, and Application to Image Classification}, |
| year |
= |
{2005}, |
| month |
= |
{janvier}, |
| journal |
= |
{Applicable Analysis}, |
| volume |
= |
{84}, |
| number |
= |
{1}, |
| pages |
= |
{15--35}, |
| pdf |
= |
{http://www.math.u-bordeaux1.fr/~jaujol/HDR/A2.pdf}, |
| keyword |
= |
{} |
| } |
|
8 - Image Decomposition into a Bounded Variation Component and an Oscillating Component.
J.F. Aujol et G. Aubert et L. Blanc-Féraud et A. Chambolle. Journal of Mathematical Imaging and Vision, 22(1): pages 71--88, janvier 2005.
@ARTICLE{BLA05,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Image Decomposition into a Bounded Variation Component and an Oscillating Component}, |
| year |
= |
{2005}, |
| month |
= |
{janvier}, |
| journal |
= |
{Journal of Mathematical Imaging and Vision}, |
| volume |
= |
{22}, |
| number |
= |
{1}, |
| pages |
= |
{71--88}, |
| pdf |
= |
{http://link.springer.com/article/10.1007/s10851-005-4783-8}, |
| keyword |
= |
{} |
| } |
|
9 - Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution.
F. Lafarge et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 180: pages 4--12, 2005.
Mots-clés : Reconstruction en 3D, Zones urbaines, Approche bayésienne, MCMC, Imagerie satellitaire.
Copyright : SFPT
@ARTICLE{lafarge_sfpt05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution}, |
| year |
= |
{2005}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| volume |
= |
{180}, |
| pages |
= |
{4--12}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_sfpt05.pdf}, |
| keyword |
= |
{Reconstruction en 3D, Zones urbaines, Approche bayésienne, MCMC, Imagerie satellitaire} |
| } |
|
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2 Thèses de Doctorat et Habilitations |
1 - Constance de largeur et désocclusion dans les images digitales.
E. Villéger. Thèse de Doctorat, Universite de Nice Sophia Antipolis, décembre 2005.
@PHDTHESIS{villeger_these,
|
| author |
= |
{Villéger, E.}, |
| title |
= |
{Constance de largeur et désocclusion dans les images digitales}, |
| year |
= |
{2005}, |
| month |
= |
{décembre}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{ http://www-sop.inria.fr/dias/Theses/phd-12.php}, |
| keyword |
= |
{} |
| } |
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