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Publications of 2005
Result of the query in the list of publications :
9 Articles |
1 - Detecting codimension-two objects in an image with Ginzburg-Landau models.
G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005.
Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images.
@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 |
= |
{November}, |
| 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 |
= |
{Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} |
| } |
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 and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, October 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 |
= |
{October}, |
| 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 and G. Scarpa and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 43(8): pages 1901-1911, August 2005.
Keywords : Classification, Segmentation, Markov Fields.
@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 |
= |
{August}, |
| 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, Markov Fields} |
| } |
|
4 - Dual Norms and Image Decomposition Models.
J.F. Aujol and A. Chambolle. International Journal of Computer Vision, 63(1): pages 85-104, June 2005.
Keywords : Image decomposition.
@ARTICLE{AujolChambolle,
|
| author |
= |
{Aujol, J.F. and Chambolle, A.}, |
| title |
= |
{Dual Norms and Image Decomposition Models}, |
| year |
= |
{2005}, |
| month |
= |
{June}, |
| 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 |
= |
{Image decomposition} |
| } |
|
5 - Invariant Bayesian estimation on manifolds.
I. H. Jermyn. Annals of Statistics, 33(2): pages 583--605, April 2005.
Keywords : Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's.
@ARTICLE{jermyn_annstat05,
|
| author |
= |
{Jermyn, I. H.}, |
| title |
= |
{Invariant Bayesian estimation on manifolds}, |
| year |
= |
{2005}, |
| month |
= |
{April}, |
| 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 |
= |
{Bayesian estimation, MAP, MMSE, Invariant, Metric, 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. |
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6 - Modeling very Oscillating Signals. Application to Image Processing.
G. Aubert and J.F. Aujol. Applied Mathematics and Optimization, 51(2): pages 163--182, March 2005.
@ARTICLE{AujolAubert,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Modeling very Oscillating Signals. Application to Image Processing}, |
| year |
= |
{2005}, |
| month |
= |
{March}, |
| 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 and J.F. Aujol. Applicable Analysis, 84(1): pages 15--35, January 2005.
@ARTICLE{AujolAubertclassif,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Optimal Partitions, Regularized Solutions, and Application to Image Classification}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| 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 and G. Aubert and L. Blanc-Féraud and A. Chambolle. Journal of Mathematical Imaging and Vision, 22(1): pages 71--88, January 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 |
= |
{January}, |
| 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 and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 180: pages 4--12, 2005.
Keywords : 3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images.
Copyright : SFPT
@ARTICLE{lafarge_sfpt05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{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 |
= |
{3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images} |
| } |
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2 PhD Thesis and Habilitations |
1 - Constance de largeur et désocclusion dans les images digitales.
E. Villéger. PhD Thesis, Universite de Nice Sophia Antipolis, December 2005.
@PHDTHESIS{villeger_these,
|
| author |
= |
{Villéger, E.}, |
| title |
= |
{Constance de largeur et désocclusion dans les images digitales}, |
| year |
= |
{2005}, |
| month |
= |
{December}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{ http://www-sop.inria.fr/dias/Theses/phd-12.php}, |
| keyword |
= |
{} |
| } |
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