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Publications de Josiane Zerubia
Résultat de la recherche dans la liste des publications :
64 Rapports de recherche et Rapports techniques |
9 - Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept.
P. Pankajakshan et L. Blanc-Féraud et B. Zhang et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Rapport de Recherche 6493, INRIA, avril 2008.
Mots-clés : Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Variation totale.
Copyright : ARIANA/INRIA
@TECHREPORT{ppankajakshan08b,
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| author |
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{Pankajakshan, P. and Blanc-Féraud, L. and Zhang, B. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
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{Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept}, |
| year |
= |
{2008}, |
| month |
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{avril}, |
| institution |
= |
{INRIA}, |
| type |
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{Research Report}, |
| number |
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{6493}, |
| url |
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{https://hal.inria.fr/inria-00269265}, |
| pdf |
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{http://hal.inria.fr/docs/00/27/02/92/PDF/report.pdf}, |
| keyword |
= |
{Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Variation totale} |
| } |
Résumé :
Nous proposons une méthode de restauration itérative d’images de fluorescence
CLSM et d’estimation paramétrique de la fonction de flou (PSF) du système d’acquisition.
Le CLSM est un microscope qui balaye un échantillon en 3D et utilise une sténopée pour
rejeter la lumière en dehors du point de focalisation. Néanmoins, la qualité des images
souffre de deux limitations physiques. La première est due à la diffraction due au système
optique et la seconde est due à la quantité réduite de lumière détectée par le tube
photo-multiplicateur (PMT). Ces limitations induisent respectivement un flou et du bruit
de comptage de photons. Les images peuvent alors bénéficier d’un post-traitement de
restauration fondé sur la déconvolution. Le problème à traiter est l’estimation simultanée
de la distribution 3D de l’échantillon des sources fluorescentes et de la PSF du microscope
(i.e. de déconvolution aveugle). En utilisant un modèle de processus physique
d’acquisition d’images microscopiques (CLSM), on réduit le nombre de paramètres libres
décrivant la PSF et on introduit des contraintes. On introduit aussi des connaissances a
priori sur l’échantillon ce qui permet de stabiliser le processus d’estimation et de favoriser
la convergence. Des expériences sur des données synthétiques montrent que la PSF peut
être estimée avec précision. Des expériences sur des données réelles montrent de bons
resultats de déconvolution en comparaison avec le modèle théorique de la PSF du microscope. |
Abstract :
We propose a method for the iterative restoration of fluorescence Confocal Laser Scanning Microscope (CLSM) images with 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 image suffers from two primary physical limitations. The first is due to the diffraction-limited nature of the optical system and the second is due to the reduced amount of light detected by the photomultiplier tube (PMT). These limitations cause blur and photon counting noise respectively. The 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 observation data. We also introduce a priori knowledge of the specimen that permits stabilization of the estimation process and favorizes the convergence. Experiments on simulated data show that the PSF could be estimatedwith a higher degree of accuracy and those done on real data show very good deconvolution results in comparison to the theoretical microscope PSF model. |
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10 - Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier.
M. Carlavan et P. Weiss et L. Blanc-Féraud et J. Zerubia. Rapport de Recherche 6732, INRIA, 2008.
Mots-clés : l1 norm, nesterov scheme, total variation minimization, wavelet.
Copyright :
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{Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, |
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{Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier}, |
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{2008}, |
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{l1 norm, nesterov scheme, total variation minimization, wavelet} |
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|
11 - Support Vector Machines for burnt area discrimination.
O. Zammit et X. Descombes et J. Zerubia. Rapport de Recherche 6343, INRIA, novembre 2007.
Mots-clés : Feux de foret, Zones brûlées, Imagerie satellitaire, Support Vector Machines, Classification.
@TECHREPORT{zammit_RR_07,
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{Zammit, O. and Descombes, X. and Zerubia, J.}, |
| title |
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{Support Vector Machines for burnt area discrimination}, |
| year |
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{2007}, |
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{novembre}, |
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{INRIA}, |
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{Feux de foret, Zones brûlées, Imagerie satellitaire, Support Vector Machines, Classification} |
| } |
Résumé :
Ce rapport aborde le problème de l'évaluation des dégâts après un feux de forêt. La détection est effectuée à partir d'une seule image satellite (SPOT 5) acquise après le feu. Afin de détecter les zones brûlées, nous utilisons une approche récente de classification nommée SVM (Séparateurs à Vaste Marge). Cette méthode est comparée aux algorithmes de classification plus conventionnels comme les K-moyennes ou les K-plus proches voisins, qui sont régulièrement utilisés en traitement d'image. Nous proposons également une méthode de classification non supervisée combinant les K-moyennes et les SVM. Les résultats fournis par les différentes techniques sont comparés à des vérités de terrain sur diverses zones brûlées. |
Abstract :
This report addresses the problem of burnt area discrimination using remote sensing images. The detection is based on a single post-fire image acquired by SPOT 5 satellite. To delineate the burnt areas, we use a recent classification method called Support Vectors Machines (SVM). This approach is compared to more conventional classifiers such as K-means or K-nearest neighbours which are widely used in image processing. We also proposed a new automatic classification approach combining K-means and SVM. The results given by the different methods are finally compared to ground truths on various burnt areas |
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12 - Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations.
S. Descamps et X. Descombes et A. Béchet et J. Zerubia. Research Report 6328, INRIA, octobre 2007.
Mots-clés : Extraction d'objets, modélisation stochastique , Processus ponctuels marques, dynamique de naissance/mort, environnement, flamants roses.
@TECHREPORT{Descamps-Descombes,
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{Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations}, |
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{2007}, |
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{6328}, |
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{http://hal.inria.fr/docs/00/18/08/93/PDF/RR-Desc-Desc-Bech-Zeru.pdf}, |
| keyword |
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{Extraction d'objets, modélisation stochastique , Processus ponctuels marques, dynamique de naissance/mort, environnement, flamants roses} |
| } |
|
13 - An adaptive simulated annealing cooling schedule for object detection in images.
M. Ortner et X. Descombes et J. Zerubia. Rapport de Recherche 6336, INRIA, octobre 2007.
Mots-clés : Traitement d'image, Shape extraction, Spatial point process, Recuit Simule, Adaptive cooling schedule.
@TECHREPORT{Ortner-Descombes,
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| author |
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{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
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{An adaptive simulated annealing cooling schedule for object detection in images}, |
| year |
= |
{2007}, |
| month |
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{octobre}, |
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{INRIA}, |
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| url |
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{https://hal.inria.fr/inria-00181764}, |
| pdf |
= |
{https://hal.inria.fr/inria-00181764}, |
| keyword |
= |
{Traitement d'image, Shape extraction, Spatial point process, Recuit Simule, Adaptive cooling schedule} |
| } |
|
14 - A Three-layer MRF model for Object Motion Detection in Airborne Images.
C. Benedek et T. Szirányi et Z. Kato et J. Zerubia. Rapport de Recherche 6208, INRIA, juin 2007.
Mots-clés : Aerial images, Change detection, Camera motion, MRF.
@TECHREPORT{benedek_INRIARR07,
|
| author |
= |
{Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, |
| title |
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{A Three-layer MRF model for Object Motion Detection in Airborne Images}, |
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{2007}, |
| month |
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{juin}, |
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{INRIA}, |
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= |
{6208}, |
| url |
= |
{https://hal.inria.fr/inria-00150805}, |
| pdf |
= |
{https://hal.inria.fr/inria-00150805}, |
| keyword |
= |
{Aerial images, Change detection, Camera motion, MRF} |
| } |
|
15 - Hierarchical finite-state modeling for texture segmentation with application to forest classification.
G. Scarpa et M. Haindl et J. Zerubia. Research Report 6066, INRIA, INRIA, France, décembre 2006.
Mots-clés : Texture, Segmentation, Co-occurrence matrix, Approche structurelle, MCMC, Synthesis.
@TECHREPORT{scarparr06,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{Hierarchical finite-state modeling for texture segmentation with application to forest classification}, |
| year |
= |
{2006}, |
| month |
= |
{décembre}, |
| institution |
= |
{INRIA}, |
| type |
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{Research Report}, |
| number |
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{6066}, |
| address |
= |
{INRIA, France}, |
| url |
= |
{https://hal.inria.fr/inria-00118420}, |
| keyword |
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{Texture, Segmentation, Co-occurrence matrix, Approche structurelle, MCMC, Synthesis} |
| } |
Abstract :
In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- and inter-texture interactions, and eventually addresses the segmentation task in a completely unsupervised manner. Moreover, it provides a hierarchical output, as the user may decide the scale at which the segmentation has to be given. Tests were carried out on both natural texture mosaics provided by the Prague Texture Segmentation Datagenerator Benchmark and remote-sensing data of forest areas provided by the French National Forest Inventory (IFN). |
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16 - A higher-order active contour model of a `gas of circles' and its application to tree crown extraction.
P. Horvath et I. H. Jermyn et Z. Kato et J. Zerubia. Research Report 6026, INRIA, France, novembre 2006.
Mots-clés : Extraction de Houppiers, Aerial images, Ordre superieur, Contour actif, Gaz de cercles, Forme.
@TECHREPORT{Horvath05,
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{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
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{A higher-order active contour model of a `gas of circles' and its application to tree crown extraction}, |
| year |
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{2006}, |
| month |
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{novembre}, |
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{INRIA}, |
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{Research Report}, |
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{6026}, |
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| keyword |
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{Extraction de Houppiers, Aerial images, Ordre superieur, Contour actif, Gaz de cercles, Forme} |
| } |
Abstract :
Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, \eg medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques. |
|
17 - A structural approach for 3D building reconstruction.
F. Lafarge et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Research Report 6048, INRIA, novembre 2006.
Mots-clés : Reconstruction en 3D, Approche structurelle, Building, RJMCMC, Viterbi.
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{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
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{A structural approach for 3D building reconstruction}, |
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{Reconstruction en 3D, Approche structurelle, Building, RJMCMC, Viterbi} |
| } |
|
18 - An automatic building extraction method : Application to the 3D-city modeling.
F. Lafarge et P. Trontin et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Research Report 5925, INRIA, France, mai 2006.
Mots-clés : Extraction d'objets, Processus ponctuels marques, Reconstruction en 3D, Zones urbaines, Imagerie satellitaire, Modele numerique d'elevation (MNE).
@TECHREPORT{lafarge_rr_may06,
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{An automatic building extraction method : Application to the 3D-city modeling}, |
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| } |
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