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Publications de 2007
Résultat de la recherche dans la liste des publications :
28 Articles de conférence |
12 - Parametric blind deconvolution for confocal laser scanning microscopy.
P. Pankajakshan et B. Zhang et L. Blanc-Féraud et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Dans Proc. 29th International Conference of IEEE EMBS (EMBC-07), pages 6531-6534, août 2007.
Mots-clés : Microscopie confocale, Blind Deconvolution, Poisson noise, Variation totale, Algorithme EM, Estimation bayesienne.
Copyright : ©2007 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.
@INPROCEEDINGS{Pankajakshan07a,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Parametric blind deconvolution for confocal laser scanning microscopy}, |
| year |
= |
{2007}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. 29th International Conference of IEEE EMBS (EMBC-07)}, |
| pages |
= |
{6531-6534}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/4352184/4352185/04353856.pdf?tp=&isnumber=&arnumber=4353856}, |
| keyword |
= |
{Microscopie confocale, Blind Deconvolution, Poisson noise, Variation totale, Algorithme EM, Estimation bayesienne} |
| } |
Abstract :
In this paper, we propose a method for the
iterative restoration of fluorescence Confocal Laser Scanning
Microscopic (CLSM) images and parametric estimation of the
acquisition system’s Point Spread Function (PSF). The CLSM is
an optical fluorescence microscope that scans a specimen in 3D
and uses a pinhole to reject most of the out-of-focus light. However,
the quality of the images suffers from two basic physical
limitations. The diffraction-limited nature of the optical system,
and the reduced amount of light detected by the photomultiplier
cause blur and photon counting noise respectively. These images
can hence benefit from post-processing restoration methods
based on deconvolution. An efficient method for parametric
blind image deconvolution involves the simultaneous estimation
of the specimen 3D distribution of fluorescent sources and
the microscope PSF. By using a model for the microscope
image acquisition physical process, we reduce the number of
free parameters describing the PSF and introduce constraints.
The parameters of the PSF may vary during the course of
experimentation, and so they have to be estimated directly from
the observed data. A priori model of the specimen is further
applied to stabilize the alternate minimization algorithm and to
converge to the solutions. |
|
13 - Assessment of different classification algorithms for burnt land discrimination.
O. Zammit et X. Descombes et J. Zerubia. Dans Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 3000-3003, Barcelone, Spain, juillet 2007.
Mots-clés : Imagerie satellitaire, Zones brûlées, Support Vector Machines, Feux de foret, Classification.
Copyright : IEEE
|
14 - A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images.
G. Scarpa et M. Haindl et J. Zerubia. Dans Scandinavian Conference on Image Analysis, Vol. 4522/2007, pages 303-312, series LNCS 4522, Ed. Springer Berlin / Heidelberg, Aalborg, Denmark, juin 2007.
@INPROCEEDINGS{scarpa_scia_07,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Scandinavian Conference on Image Analysis}, |
| volume |
= |
{4522/2007}, |
| pages |
= |
{303-312}, |
| series |
= |
{LNCS 4522}, |
| editor |
= |
{Springer Berlin / Heidelberg}, |
| address |
= |
{Aalborg, Denmark}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-73040-8_31}, |
| keyword |
= |
{} |
| } |
|
15 - A Novel Representation for Riemannian Analysis of Elastic Curves in R^n.
S. Joshi et E. Klassen et A. Srivastava et I. H. Jermyn. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, juin 2007.
Mots-clés : Forme, Metrique, Geodesic, A priori.
@INPROCEEDINGS{Joshi07a,
|
| author |
= |
{Joshi, S. and Klassen, E. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{A Novel Representation for Riemannian Analysis of Elastic Curves in R^n}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Minneapolis, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/CVPR.2007.383185}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07a.pdf}, |
| keyword |
= |
{Forme, Metrique, Geodesic, A priori} |
| } |
Abstract :
We propose an efficient representation for studying shapes of closed curves in R^n. This paper combines the strengths of two important ideas---elastic shape metric and path-straightening methods---and results in a very fast algorithm for finding geodesics in shape spaces. The elastic metric allows for optimal matching of features between the two curves while path-straightening ensures that the algorithm results in geodesic paths. For the novel representation proposed here, the elastic metric becomes the simple L^2 metric, in contrast to the past usage where more complex forms were used. We present the step-by-step algorithms for computing geodesics and demonstrate them with 2-D as well as 3-D examples. |
|
16 - Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface.
A. Bhattacharya et M. Roux et H. Maitre et I. H. Jermyn et X. Descombes et J. Zerubia. Dans Proc. International Workshop on Content-Based Multimedia Indexing, Bordeaux, France, juin 2007.
Mots-clés : Indexation, Reseaux routiers, Semantique, Retrieval, Feature statistics.
@INPROCEEDINGS{Bhattacharya07a,
|
| author |
= |
{Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Indexing Satellite Images with Features Computed from Man-Made Structures on the Earth’s Surface}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. International Workshop on Content-Based Multimedia Indexing}, |
| address |
= |
{Bordeaux, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07a.pdf}, |
| keyword |
= |
{Indexation, Reseaux routiers, Semantique, Retrieval, Feature statistics} |
| } |
Abstract :
Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database. |
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17 - Riemannian Analysis of Probability Density Functions with Applications in Vision.
S. Joshi et A. Srivastava et I. H. Jermyn. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Minneapolis, USA, juin 2007.
Mots-clés : Probability density function, Metrique, Geodesic, Reparameterization.
@INPROCEEDINGS{Joshi07,
|
| author |
= |
{Joshi, S. and Srivastava, A. and Jermyn, I. H.}, |
| title |
= |
{Riemannian Analysis of Probability Density Functions with Applications in Vision}, |
| year |
= |
{2007}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Minneapolis, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/CVPR.2007.383188 }, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Joshi07.pdf}, |
| keyword |
= |
{Probability density function, Metrique, Geodesic, Reparameterization} |
| } |
Abstract :
Applications in computer vision involve statistically analyzing an important class of constrained, non- negative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian structure on the spaces formed by these functions. We propose a em spherical version of the Fisher-Rao metric that provides closed form expressions for geodesics and distances, and allows an efficient computation of statistics. We compare this metric with some previously used metrics and present an application in planar shape classification. |
|
18 - A Hierarchical finite-state model for texture segmentation.
G. Scarpa et M. Haindl et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 1, pages 1209-1212, Honolulu, HI (USA), avril 2007.
@INPROCEEDINGS{scarpa_icassp_07,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{A Hierarchical finite-state model for texture segmentation}, |
| year |
= |
{2007}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| volume |
= |
{1}, |
| pages |
= |
{1209-1212}, |
| address |
= |
{Honolulu, HI (USA)}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4217303}, |
| keyword |
= |
{} |
| } |
|
19 - Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry.
T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. Urban, Paris, France, avril 2007.
Mots-clés : Reseaux routiers, Very high resolution, Multiscale, Ordre superieur, Contour actif, Forme.
@INPROCEEDINGS{Peng07_urban,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry}, |
| year |
= |
{2007}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Proc. Urban}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Peng07urban.pdf}, |
| keyword |
= |
{Reseaux routiers, Very high resolution, Multiscale, Ordre superieur, Contour actif, Forme} |
| } |
Abstract :
This paper addresses the problem of automatically
extracting the main road network in a dense urban area from
a very high resolution optical satellite image using a variational
approach. The model energy has two parts: a phase field higherorder
active contour energy that describes our prior knowledge
of road network geometry, i.e. that it is composed of elongated
structures with roughly parallel borders that meet at junctions;
and a multi-scale statistical image model describing the image
we expect to see given a road network. By minimizing the model
energy, an estimate of the road network is obtained. Promising
results on 0.6m QuickBird Panchromatic images are presented,
and future improvements to the models are outlined. |
|
20 - Image deconvolution using a stochastic differential equation approach.
X. Descombes et M. Lebellego et E. Zhizhina. Dans Proc. nternational Conference on Computer Vision Theory
and Applications, Barcelona, Spain, mars 2007.
Mots-clés : Deconvolution, Stochastic Differential Equation.
@INPROCEEDINGS{xavBarca2,
|
| author |
= |
{Descombes, X. and Lebellego, M. and Zhizhina, E.}, |
| title |
= |
{Image deconvolution using a stochastic differential equation approach}, |
| year |
= |
{2007}, |
| month |
= |
{mars}, |
| booktitle |
= |
{Proc. nternational Conference on Computer Vision Theory
and Applications}, |
| address |
= |
{Barcelona, Spain}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_xavBarca2.pdf}, |
| keyword |
= |
{Deconvolution, Stochastic Differential Equation} |
| } |
|
21 - Circular object segmentation using higher-order active contours.
P. Horvath et I. H. Jermyn et Z. Kato et J. Zerubia. Dans In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07), Debrecen, Hungary, janvier 2007.
Note : In Hungarian
Mots-clés : Ordre superieur, Extraction de Houppiers, Forme.
@INPROCEEDINGS{Horvath07a,
|
| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{Circular object segmentation using higher-order active contours}, |
| year |
= |
{2007}, |
| month |
= |
{janvier}, |
| booktitle |
= |
{In Proc. Conference of the Hungarian Association for Image Analysis and Pattern Recognition (KEPAF'07)}, |
| address |
= |
{Debrecen, Hungary}, |
| note |
= |
{In Hungarian}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07a.pdf}, |
| keyword |
= |
{Ordre superieur, Extraction de Houppiers, Forme} |
| } |
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