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Publications de 2011
Résultat de la recherche dans la liste des publications :
7 Articles |
1 - Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE Journal of Selected Topics in Signal Processing, 5(3): pages 554-566, juin 2011.
Mots-clés : Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula.
Copyright : IEEE
@ARTICLE{krylovJSTSP2011,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas}, |
| year |
= |
{2011}, |
| month |
= |
{juin}, |
| journal |
= |
{ IEEE Journal of Selected Topics in Signal Processing}, |
| volume |
= |
{5}, |
| number |
= |
{3}, |
| pages |
= |
{554-566}, |
| url |
= |
{http://dx.doi.org/10.1109/JSTSP.2010.2103925}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00562326/en/}, |
| keyword |
= |
{Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula} |
| } |
Abstract :
In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene. |
|
2 - An automatic counter for aerial images of aggregations of large birds.
S. Descamps et A. Béchet et X. Descombes et A. Arnaud et J. Zerubia. Bird Study, : pages 1-7, juin 2011.
@ARTICLE{BirdStudy,
|
| author |
= |
{Descamps, S. and Béchet, A. and Descombes, X. and Arnaud, A. and Zerubia, J.}, |
| title |
= |
{An automatic counter for aerial images of aggregations of large birds}, |
| year |
= |
{2011}, |
| month |
= |
{juin}, |
| journal |
= |
{Bird Study}, |
| pages |
= |
{1-7}, |
| url |
= |
{http://hal.inria.fr/inria-00624587}, |
| pdf |
= |
{http://www-sop.inria.fr/ariana/Publis/Descamps2011BS.pdf}, |
| keyword |
= |
{} |
| } |
|
3 - On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection.
P. Weiss et A. Fournier et L. Blanc-Féraud et G. Aubert. SIAM Journal on Imaging Sciences, 4(1): pages 448-471, mars 2011.
Mots-clés : Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing.
@ARTICLE{SIIMS_2011,
|
| author |
= |
{Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection}, |
| year |
= |
{2011}, |
| month |
= |
{mars}, |
| journal |
= |
{SIAM Journal on Imaging Sciences}, |
| volume |
= |
{4}, |
| number |
= |
{1}, |
| pages |
= |
{448-471}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, |
| pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, |
| keyword |
= |
{Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing} |
| } |
Abstract :
We analyze the illumination invariance of the level lines of an image. We show that if the scene
surface has Lambertian reflectance and the light is directed, then a necessary and sufficient condition
for the level lines to be illumination invariant is that the three-dimensional scene be developable and
that its albedo satisfy some geometrical constraints. We then show that the level lines are “almost”
invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. This
allows us to devise a fast and simple algorithm that detects changes between pairs of remotely
sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of
the algorithm both on synthetic OpenGL scenes and real QuickBird images. The synthetic results
illustrate the theory developed in this paper. The two real QuickBird images show that the proposed
change detection algorithm is discriminant. For easy scenes it achieves a rate of 85% detected changes
for 10% false positives, while it reaches a rate of 75% detected changes for 25% false positives on
demanding scenes.
|
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4 - Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE-Geoscience and Remote Sensing Letters, 8(1): pages 148-152, janvier 2011.
Mots-clés : finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (SEM), synthetic aperture radar.
Copyright : IEEE
@ARTICLE{krylovGRSL2011,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data}, |
| year |
= |
{2011}, |
| month |
= |
{janvier}, |
| journal |
= |
{IEEE-Geoscience and Remote Sensing Letters}, |
| volume |
= |
{8}, |
| number |
= |
{1}, |
| pages |
= |
{148-152}, |
| url |
= |
{http://dx.doi.org/10.1109/LGRS.2010.2053517}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00503893/en/}, |
| keyword |
= |
{finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (SEM), synthetic aperture radar} |
| } |
Abstract :
In this letter, we address the problem of estimating the amplitude probability density function (pdf) of single-channel synthetic aperture radar (SAR) images. A novel flexible method is developed to solve this problem, extending the recently proposed dictionary-based stochastic expectation maximization approach (developed for a medium-resolution SAR) to very high resolution (VHR) satellite imagery, and enhanced by introduction of a novel procedure for estimating the number of mixture components, that permits to reduce appreciably its computational complexity. The specific interest is the estimation of heterogeneous statistics, and the developed method is validated in the case of the VHR SAR imagery, acquired by the last-generation satellite SAR systems, TerraSAR-X and COSMO-SkyMed. This VHR imagery allows the appreciation of various ground materials resulting in highly mixed distributions, thus posing a difficult estimation problem that has not been addressed so far. We also conduct an experimental study of the extended dictionary of state-of-the-art SAR-specific pdf models and consider the dictionary refinements. |
|
5 - Multiple Birth and Cut Algorithm for Multiple Object Detection.
A. Gamal Eldin et X. Descombes et Charpiat G. et J. Zerubia. Journal of Multimedia Processing and Technologies, 2011.
Mots-clés : Markov point process, Multiple Birth and Cut, Graph Cut, Belief Propagation, flamingo counting.
@ARTICLE{MBC_BP10,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, |
| title |
= |
{Multiple Birth and Cut Algorithm for Multiple Object Detection}, |
| year |
= |
{2011}, |
| journal |
= |
{Journal of Multimedia Processing and Technologies}, |
| url |
= |
{http://hal.inria.fr/hal-00616371}, |
| keyword |
= |
{Markov point process, Multiple Birth and Cut, Graph Cut, Belief Propagation, flamingo counting} |
| } |
Abstract :
In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing their respective advantages and drawbacks, where the most important advantage of the MBC is the reduction of number of parameters. We demonstrate that by proposing good candidates throughout the selection phase in the birth step, the speed of convergence is increased. In this selection phase, the best candidates are chosen from object sets by a belief propagation algorithm. We validate our algorithm on the flamingo counting problem in a colony and demonstrate that our algorithm outperforms the MBD algorithm. |
|
6 - A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images.
M. S. Kulikova et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. International Journal of Computer Vision and Image Processing, 1(2): pages 1-12, 2011.
Mots-clés : Contour actif, Processus ponctuels marques, multiple birth-and-death dynamics, multiple object extraction, Shape prior.
@ARTICLE{kulikova_ijcvip2010,
|
| author |
= |
{Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images}, |
| year |
= |
{2011}, |
| journal |
= |
{International Journal of Computer Vision and Image Processing}, |
| volume |
= |
{1}, |
| number |
= |
{2}, |
| pages |
= |
{1-12}, |
| url |
= |
{http://hal.archives-ouvertes.fr/hal-00804118}, |
| keyword |
= |
{Contour actif, Processus ponctuels marques, multiple birth-and-death dynamics, multiple object extraction, Shape prior} |
| } |
Abstract :
Object extraction from images is one of the most important tasks in remote sensing image analysis. For accurate extraction from very high resolution (VHR) images, object geometry needs to be taken into account. A method for incorporating strong yet flexible prior shape information into a marked point process model for the extraction of multiple objects of complex shape is presented. To control the computational complexity, the objects considered are defined using the image data and the prior shape information. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process on the space of multiple objects. The authors present several experimental results on the extraction of tree crowns from VHR aerial images. |
|
7 - Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires.
S. Ben Hadj et F. Chatelain et X. Descombes et J. Zerubia. Revue Française de Photogrammétrie et de Télédétection (SFPT), (194): pages 2-15, 2011.
Mots-clés : processus ponctuel marqué, RJMCMC, Recuit Simule, SEM, pseudo-vraisemblance, extraction d'objet..
@ARTICLE{RFPT_SBH_11,
|
| author |
= |
{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires}, |
| year |
= |
{2011}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| number |
= |
{194}, |
| pages |
= |
{2-15}, |
| url |
= |
{http://hal.inria.fr/hal-00638665}, |
| keyword |
= |
{processus ponctuel marqué, RJMCMC, Recuit Simule, SEM, pseudo-vraisemblance, extraction d'objet.} |
| } |
|
 haut de la page
Thèse de Doctorat et Habilitation |
1 - Processus ponctuels et algorithmes de coupure minimal de graphe appliqués à l'extraction d'objets 2D et 3D.
A. Gamal Eldin. Thèse de Doctorat, Universite de Nice Sophia Antipolis, octobre 2011.
Mots-clés : Multiple object detection, Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process, Stochastic geometry.
@PHDTHESIS{GamalPhdThesis,
|
| author |
= |
{Gamal Eldin, A.}, |
| title |
= |
{Processus ponctuels et algorithmes de coupure minimal de graphe appliqués à l'extraction d'objets 2D et 3D}, |
| year |
= |
{2011}, |
| month |
= |
{octobre}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{http://tel.archives-ouvertes.fr/tel-00737988}, |
| keyword |
= |
{Multiple object detection, Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process, Stochastic geometry} |
| } |
Résumé :
L'objectif de cette thèse est de développer une nouvelle approche de détection d'objets 3D à partir d'une image 2D, prenant en compte les occultations et les phénomènes de perspective. Cette approche est fondée sur la théorie des processus ponctuels marqués, qui a fait ses preuves dans la solution de plusieurs problèmes en imagerie haute résolution. Le travail de la thèse est structuré en deux parties: En première partie: Nous proposons une nouvelle méthode probabiliste pour gérer les occultations et les effets de perspective. Le modèle propose est fondé sur la simulation d'une scène 3D utilisant OpenGL sur une carte graphique (GPU). C'est une méthode orientée objet, intégrée dans le cadre d'un processus ponctuel marqué. Nous l'appliquons pour l'estimation de la taille d'une colonie de manchots, là où nous modélisons une colonie de manchots comme un nombre inconnu d'objets 3D. L'idée principale de l'approche proposée consiste à échantillonner certaines configurations candidat composé d'objets 3D s'appuyant sur le plan réel. Une densité de Gibbs est définie sur l'espace des configurations, qui prend en compte des informations a priori et sur les données. Pour une configuration proposée, la scène est projetée sur le plan image, et les configurations sont modifiées jusqu'à convergence. Pour évaluer une configuration proposée, nous mesurons la similarité entre l'image projetée de la configuration proposée et l'image réelle, définissant ainsi le terme d'attache aux données et l'a priori pénalisant les recouvrements entre objets. Nous avons introduit des modifications dans l'algorithme d'optimisation pour prendre en compte les nouvelles dépendances qui existent dans notre modèle 3D. En deuxième partie: Nous proposons une nouvelle méthode d'optimisation appelée |
Abstract :
The topic of this thesis is to develop a novel approach for 3D object detection from a 2D image. This approach takes into consideration the occlusions and the perspective effects. This work has been embedded in a marked point process framework, proved to be efficient for solving many challenging problems dealing with high resolution images. The accomplished work during the thesis can be presented in two parts: First part: We propose a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is based on 3D scene simulation on the GPU using OpenGL. It is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying on the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. The proposed configurations are projected onto the image plane, and the configurations are modified until convergence. To evaluate a proposed configuration, we measure the similarity between the projected image of the proposed configuration and the real image, by defining a data term and a prior term which penalize objects overlapping. We introduced modifications to the optimization algorithm to take into account new dependencies that exists in our 3D model. Second part: We propose a new optimization method which we call |
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haut de la page
19 Articles de conférence |
1 - Building large urban environments from unstructured point data.
F. Lafarge et C. Mallet. Dans Proc. IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, novembre 2011.
@INPROCEEDINGS{lafarge_iccv11,
|
| author |
= |
{Lafarge, F. and Mallet, C.}, |
| title |
= |
{Building large urban environments from unstructured point data}, |
| year |
= |
{2011}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Barcelona, Spain}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126353}, |
| keyword |
= |
{} |
| } |
|
2 - Synthetic Aperture Radar Image Classification via Mixture Approaches.
V. Krylov et J. Zerubia. Dans Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, novembre 2011.
Mots-clés : Radar a Ouverture Synthetique (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution.
Copyright : IEEE
@INPROCEEDINGS{krylovCOMCAS11,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{Synthetic Aperture Radar Image Classification via Mixture Approaches}, |
| year |
= |
{2011}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, |
| address |
= |
{Tel Aviv, Israel}, |
| url |
= |
{http://www.ortra.biz/comcas/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00625551/en/}, |
| keyword |
= |
{Radar a Ouverture Synthetique (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} |
| } |
Abstract :
In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors. |
|
3 - Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil.
J. Zhou et C. Proisy et X. Descombes et J. Zerubia et G. Le Maire et Y. Nouvellon et P. Couteron. Dans Asian Conference on Pattern Recognition (ACPR), Beijing, China, novembre 2011.
Mots-clés : tree detection, Eucalyptus plantation, Marked point process, multi-date detection.
@INPROCEEDINGS{Zhou11,
|
| author |
= |
{Zhou, J. and Proisy, C. and Descombes, X. and Zerubia, J. and Le Maire, G. and Nouvellon, Y. and Couteron, P.}, |
| title |
= |
{Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil}, |
| year |
= |
{2011}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Asian Conference on Pattern Recognition (ACPR)}, |
| address |
= |
{Beijing, China}, |
| url |
= |
{http://hal.inria.fr/hal-00740973}, |
| keyword |
= |
{tree detection, Eucalyptus plantation, Marked point process, multi-date detection} |
| } |
Abstract :
Individual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations. |
|
4 - Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images.
S. Prigent et D. Zugaj et X. Descombes et P. Martel et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
@INPROCEEDINGS{prigent11a,
|
| author |
= |
{Prigent, S. and Zugaj, D. and Descombes, X. and Martel, P. and Zerubia, J.}, |
| title |
= |
{Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/59/06/94/PDF/icip_final.pdf}, |
| keyword |
= |
{} |
| } |
Abstract :
Clinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy. |
|
5 - Two constrained formulations for deblurring Poisson noisy images.
M. Carlavan et L. Blanc-Féraud. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
Mots-clés : Poisson deconvolution, discrepancy principle, constrained convex optimization.
@INPROCEEDINGS{ICIP2011_Carlavan,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Two constrained formulations for deblurring Poisson noisy images}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00591035/fr/}, |
| keyword |
= |
{Poisson deconvolution, discrepancy principle, constrained convex optimization} |
| } |
Abstract :
Deblurring noisy Poisson images has recently been subject of an increasingly amount of works in many areas such as astronomy or biological imaging. Several methods have promoted explicit prior on the solution to regularize the ill-posed inverse problem and to improve the quality of the image. In each of these methods, a regularizing parameter is introduced to control the weight of the prior. Unfortunately, this regularizing parameter has to be manually set such that it gives the best qualitative results. To tackle this issue, we present in this paper two constrained formulations for the Poisson deconvolution problem, derived from recent advances in regularizing parameter estimation for Poisson noise. We first show how to improve the accuracy of these estimators and how to link these estimators to constrained formulations. We then propose an algorithm to solve the resulting optimization problems and detail how to perform the projections on the constraints. Results on real and synthetic data are presented. |
|
6 - A fast multiple birth and cut algorithm using belief propagation.
A. Gamal Eldin et X. Descombes et Charpiat G. et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
Mots-clés : Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation.
@INPROCEEDINGS{MBC_ICIP11,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, |
| title |
= |
{A fast multiple birth and cut algorithm using belief propagation}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00592446/fr/}, |
| keyword |
= |
{Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation} |
| } |
Abstract :
In this paper, we present a faster version of the newly proposed Multiple Birth and Cut (MBC) algorithm. MBC is an optimization method applied to the energy minimization of an object based model, defined by a marked point process. We show that, by proposing good candidates in the birth step of this algorithm, the speed of convergence is increased. The algorithm starts by generating a dense configuration in a special organization, the best candidates are selected using the belief propagation algorithm. Next, this candidate configuration is combined with the current configuration using binary graph cuts as presented in the original version of the MBC algorithm. We tested the performance of our algorithm on the particular problem of counting flamingos in a colony, and show that it is much faster with the modified birth step. |
|
7 - Formulation contrainte pour la déconvolution de bruit de Poisson.
M. Carlavan et L. Blanc-Féraud. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, septembre 2011.
Mots-clés : 3D confocal microscopy, constrained convex optimization, discrepancy principle, Poisson noise.
@INPROCEEDINGS{CarlavanGRETSI11,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Formulation contrainte pour la déconvolution de bruit de Poisson}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux, France}, |
| url |
= |
{http://hal.inria.fr/inria-00602015/fr/}, |
| keyword |
= |
{3D confocal microscopy, constrained convex optimization, discrepancy principle, Poisson noise} |
| } |
Résumé :
Nous considérons le problème de la restauration d’image floue et bruitée par du bruit de Poisson. De nombreux travaux ont proposé de traiter ce problème comme la minimisation d’une énergie convexe composée d’un terme d’attache aux données et d’un terme de régularisation choisi selon l’a priori dont on dispose sur l’image à restaurer. Un des problèmes récurrents dans ce type d’approche est le choix du paramètre de régularisation qui contrôle le compromis entre l’attache aux données et la régularisation. Une approche est de choisir ce paramètre de régularisation en procédant à plusieurs minimisations pour plusieurs valeurs du paramètre et en ne gardant que celle qui donne une image restaurée vérifiant un certain critère (qu’il soit qualitatif ou quantitatif). Cette technique est évidemment très couteuse lorsque les données traitées sont de grande dimension, comme c’est le cas en microscopie 3D par exemple. Nous proposons ici de formuler le problème de restauration
d’image floue et bruitée par du bruit de Poisson comme un problème contraint sur l’antilog de la vraisemblance poissonienne et proposons une
estimation de la borne à partir des travaux de Bertero et al. sur le principe de discrepancy pour l’estimation du paramètre de régularisation en présence de bruit de Poisson. Nous montrons des résultats sur des images synthétiques et réelles et comparons avec l'écriture non-contrainte utilisant une approximation gaussienne du bruit de Poisson pour l’estimation du paramètre de régularisation. |
Abstract :
We focus here on the restoration of blurred and Poisson noisy images. Several methods solve this problem by minimizing a convex cost function composed of a data term and a regularizing term chosen from the prior that one have on the image. One of the recurrent problems of this approach is how to choose the regularizing paramater which controls the weight of the regularization term in front of the data term. One method consists in solving the minimization problem for several values of this parameter and by keeping the value which gives an image verifying a quality criterion (either qualitative or quantitative). This technique is obviously time consuming when one deal with high dimensional data such as in 3D microscopy imaging. We propose to formulate the blurred and Poisson noisy images restoration problem as a constrained problem on the antilog of the Poisson likelihood and propose an estimation of the bound from the works of Bertero et al. on the discrepancy principle for the estimation of the regularizing parameter for Poisson noise. We show results on synthetic and real data and we compare these results to the one obtained with the unconstrained formulation using the Gaussian approximation of the Poisson noise for the estimation of the regularizing parameter. |
|
8 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities.
K. Kayabol et A. Voisin et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, septembre 2011.
Mots-clés : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm.
@INPROCEEDINGS{inria-00592252,
|
| author |
= |
{Kayabol, K. and Voisin, A. and Zerubia, J.}, |
| title |
= |
{SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| pages |
= |
{173-176}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00592252/en/}, |
| keyword |
= |
{High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} |
| } |
Abstract :
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data. |
|
9 - Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution.
A. Voisin et V. Krylov et J. Zerubia. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, septembre 2011.
Mots-clés : Images SAR, Classification, Zones urbaines, Champs de Markov, Modeles hierarchiques.
@INPROCEEDINGS{VoisinGretsi2011,
|
| author |
= |
{Voisin, A. and Krylov, V. and Zerubia, J.}, |
| title |
= |
{Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux}, |
| url |
= |
{http://hal.inria.fr/inria-00623003/fr/}, |
| keyword |
= |
{Images SAR, Classification, Zones urbaines, Champs de Markov, Modeles hierarchiques} |
| } |
Résumé :
Ce papier présente un modèle de classification bayésienne supervisée d’images acquises par Radar à Synthèse d’Ouverture (RSO) très haute résolution en polarisation simple contenant des zones urbaines, particulièrement affectées par le bruit de chatoiement. Ce modèle prend en compte à la fois une représentation statistique des images RSO par modèle de mélanges finis et de copules, et une modélisation contextuelle
à partir de champs de Markov hiérarchiques. |
Abstract :
This paper deals with the Bayesian classification of single-polarized very high resolution synthetic aperture radar (SAR) images
that depict urban areas. The difficulty of such a classification relies in the significant effects of speckle noise. The model considered here takes into account both statistical modeling of images via finite mixture models and copulas, and contextual modeling thanks to hierarchical Markov random fields |
|
10 - Restauration d'image dégradée par un flou spatialement variant.
S. Ben Hadj et L. Blanc-Féraud. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, septembre 2011.
@INPROCEEDINGS{SaimaGretsi11,
|
| author |
= |
{Ben Hadj, S. and Blanc-Féraud, L.}, |
| title |
= |
{Restauration d'image dégradée par un flou spatialement variant}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux, France}, |
| url |
= |
{http://hal.inria.fr/inria-00625519/fr/}, |
| keyword |
= |
{} |
| } |
Résumé :
La plupart des techniques de restauration d’images disponibles supposent que le flou est spatialement invariant. Néanmoins, différents
phénomènes physiques liés aux propriétés de l’optique font que les dégradations peuvent être différentes selon les régions de l’image. Dans ce
travail, nous considérons un modèle de PSF invariant par zone avec des transitions régulières entre les zones afin de prendre en compte la
variation du flou dans l’image. Nous développons pour ce modèle, une méthode de déconvolution adaptée, par minimisation d’un critère avec
une régularisation par variation totale. Nous nous appuyions sur une méthode rapide de minimisation par décomposition de domaine qui a été
récemment développée par Fornasier et al., 2009. Nous obtenons ainsi un algorithme où la minimisation du critère est effectuée en parallèle sur
les différentes zones de l’image, tout en prenant en compte les estimées dans les zones voisines des sous-images considérées, de sorte que la
solution finale soit le minimum du critère où le flou est variant spatialement. |
Abstract :
In most of the existing image restoration techniques, the blur is assumed to be spatially invariant. However, different physical
phenomena related to the optic’s properties makes that degradations may change according to the image’s areas. In this work, we consider a
piecewise-varying PSF model with smooth transitions between areas in order to take into account blur variation in the image. We develop for
this model, a convenient deconvolution method by minimizing a criterion with a total variation regularization. We rely on a fast minimization
method using a domain decomposition method that was recently developed by Fornasier et al. 2009. We thus obtain an algorithm where the
criterion minimization is performed in a parallel way on different areas of the image, taking into account the estimates of neighboring areas of
the considered sub-image, so that the final solution is space-varying deconvolved. |
|
11 - Extraction et caractérisation de régions saines et pathologiques à partir de micro-tomographie RX du système vasculaire cérébral.
X. Descombes et A. Gamal Eldin et F. Plouraboue et C. Fonta et S. Serduc et G. Le Duc et T. Weitkamp. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, septembre 2011.
@INPROCEEDINGS{XavierGRETSI11,
|
| author |
= |
{Descombes, X. and Gamal Eldin, A. and Plouraboue, F. and Fonta, C. and Serduc, S. and Le Duc, G. and Weitkamp, T.}, |
| title |
= |
{Extraction et caractérisation de régions saines et pathologiques à partir de micro-tomographie RX du système vasculaire cérébral}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux, France}, |
| url |
= |
{http://hal.inria.fr/inria-00625525/fr/}, |
| keyword |
= |
{} |
| } |
Abstract :
In this paper, we consider X-ray micro-tomography representing the brain vascular network. We define the local vascular territories as the regions obtained after a watershed algorithm applied on the distance map. The obtained graph is then regularized by a Markov random field approach. The optimization is performed using a graph cut algorithm. We show that the resulting segmentation exhibits three classes corresponding to normal tissue, tumour and an intermediate region. |
|
12 - Reconstruction 3D du bâti à partir d'une seule image par naissances et morts multiples.
J.D. Durou et X. Descombes et P. Lukashevish et A. Kraushonak. Dans Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, France, septembre 2011.
@INPROCEEDINGS{DurouGretsi11,
|
| author |
= |
{Durou, J.D. and Descombes, X. and Lukashevish, P. and Kraushonak, A.}, |
| title |
= |
{Reconstruction 3D du bâti à partir d'une seule image par naissances et morts multiples}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Bordeaux, France}, |
| url |
= |
{http://hal.inria.fr/inria-00625527/fr/}, |
| keyword |
= |
{} |
| } |
Résumé :
Dans cet article, nous nous écartons de l’approche classique qui considère la reconstruction 3D comme un problème inverse et la
résout en mettant en correspondance deux images d’une paire stéréoscopique. Au contraire, nous montrons qu’il est plus simple de résoudre le
problème direct. Pour ce faire, nous proposons aléatoirement des configurations de bâtiments pour ne conserver que les plus pertinentes par un
algorithme de type naissances et morts multiples. Nous montrons notamment que cette approche ne nécessite pas un temps de calcul prohibitif,
grâce à la puissance de calcul d’OpenGL qui s’appuie sur la carte graphique. Les premiers résultats obtenus montrent la pertinence de l’approche
adoptée. En particulier, elle permet de résoudre des ambiguïtés pour lesquelles l’inversion du problème serait quasiment impossible. |
Abstract :
In this paper, contrary to the classical approach addressing the 3D reconstruction problem as an inverse problem and solving it by matching two images from a stereoscopic pair, we show that we can solve the direct problem in a simpler way. To do so, we randomly propose configurations of buildings while keeping only the most relevant ones, using a multiple births and deaths algorithm. Notably, we show that this approach does not imply a prohibitory computation time, thanks to the freeware OpenGL which exploits the graphic card. The first results show that the proposed approach is relevant. In particular, it allows solving ambiguities for which inverting the problem is almost impossible. |
|
13 - Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar.
Y. Verdié et F. Lafarge et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
Mots-clés : 3D-Modeling, shape analysis, Mesh processing.
@INPROCEEDINGS{VerdieICIP11,
|
| author |
= |
{Verdié, Y. and Lafarge, F. and Zerubia, J.}, |
| title |
= |
{Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00605623/fr/}, |
| keyword |
= |
{3D-Modeling, shape analysis, Mesh processing} |
| } |
Abstract :
We present an automatic approach for modeling buildings from aerial LiDAR data. The method produces accurate, watertight and compact meshes under planar constraints which are especially designed for urban scenes. The LiDAR point cloud is classified through a non-convex energy minimization problem in order to separate the points labeled as building. Roof structures are then extracted from this point subset, and used to control the meshing procedure. Experiments highlight the potential of our method in term of minimal rendering, accuracy and compactness |
|
14 - Morphological road segmentation in urban areas from high resolution satellite images.
R. Gaetano et J. Zerubia et G. Scarpa et G. Poggi. Dans International Conference on Digital Signal Processing, Corfu, Greece, juillet 2011.
Mots-clés : Segmentation, Classification, skeletonization , pattern recognition, shape analysis.
@INPROCEEDINGS{GaetanoDSP,
|
| author |
= |
{Gaetano, R. and Zerubia, J. and Scarpa, G. and Poggi, G.}, |
| title |
= |
{Morphological road segmentation in urban areas from high resolution satellite images}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{International Conference on Digital Signal Processing}, |
| address |
= |
{Corfu, Greece}, |
| url |
= |
{http://hal.inria.fr/inria-00618222/fr/}, |
| keyword |
= |
{Segmentation, Classification, skeletonization , pattern recognition, shape analysis} |
| } |
Abstract :
High resolution satellite images provided by the last generation
sensors significantly increased the potential of almost
all the image information mining (IIM) applications related
to earth observation. This is especially true for the extraction
of road information, task of primary interest for many remote
sensing applications, which scope is more and more extended
to complex urban scenarios thanks to the availability of highly
detailed images. This context is particularly challenging due
to such factors as the variability of road visual appearence
and the occlusions from entities like trees, cars and shadows.
On the other hand, the peculiar geometry and morphology of
man-made structures, particularly relevant in urban areas, is
enhanced in high resolution images, making this kind of information
especially useful for road detection.
In this work, we provide a new insight on the use of morphological
image analysis for road extraction in complex urban
scenarios, and propose a technique for road segmentation
that only relies on this domain. The keypoint of the technique
is the use of skeletons as powerful descriptors for road objects:
the proposed method is based on an ad-hoc skeletonization
procedure that enhances the linear structure of road segments,
and extracts road objects by first detecting their skeletons
and then associating each of them with a region of the
image. Experimental results are presented on two different
high resolution satellite images of urban areas. |
|
15 - Regularizing parameter estimation for Poisson noisy image restoration.
M. Carlavan et L. Blanc-Féraud. Dans International ICST Workshop on New Computational Methods for Inverse Problems, Paris, France, mai 2011.
Mots-clés : Parameter estimation, discrepancy principle, Poisson noise.
@INPROCEEDINGS{NCMIP11,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Regularizing parameter estimation for Poisson noisy image restoration}, |
| year |
= |
{2011}, |
| month |
= |
{mai}, |
| booktitle |
= |
{International ICST Workshop on New Computational Methods for Inverse Problems}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://hal.inria.fr/inria-00590906/fr/}, |
| keyword |
= |
{Parameter estimation, discrepancy principle, Poisson noise} |
| } |
Abstract :
Deblurring images corrupted by Poisson noise is a challeng- ing process which has devoted much research in many ap- plications such as astronomical or biological imaging. This problem, among others, is an ill-posed problem which can be regularized by adding knowledge on the solution. Several methods have therefore promoted explicit prior on the im- age, coming along with a regularizing parameter to moder- ate the weight of this prior. Unfortunately, in the domain of Poisson deconvolution, only a few number of methods have been proposed to select this regularizing parameter which is most of the time set manually such that it gives the best visual results. In this paper, we focus on the use of l1 -norm prior and present two methods to select the regularizing pa- rameter. We show some comparisons on synthetic data using classical image fidelity measures. |
|
16 - A novel algorithm for occlusions and perspective effects using a 3d object process.
A. Gamal Eldin et X. Descombes et J. Zerubia. Dans ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing), Prague, Czech Republic, mai 2011.
Mots-clés : Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting.
@INPROCEEDINGS{ICASSP_2011,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A novel algorithm for occlusions and perspective effects using a 3d object process}, |
| year |
= |
{2011}, |
| month |
= |
{mai}, |
| booktitle |
= |
{ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing)}, |
| address |
= |
{Prague, Czech Republic}, |
| url |
= |
{http://hal.inria.fr/inria-00592449/fr/}, |
| keyword |
= |
{Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting} |
| } |
Abstract :
In this paper, we introduce a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying in the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. These configurations are projected onto the image plane. The configurations are modified until convergence using the multiple birth and death optimization algorithm and by measuring the similarity between the projected image of the configuration and the real image. During optimization, the proposed configuration is modeled by a mixed graph which represents all dependencies between the objects, including interaction between neighbor objects and parent-child dependency for occluded objects. We tested our model on synthetic image, and real images. |
|
17 - A new variational method for preserving point-like and curve-like singularities in 2d images.
D. Graziani et L. Blanc-Féraud et G. Aubert. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, mai 2011.
Mots-clés : Convex optimization, nesterov scheme, laplacian operator.
@INPROCEEDINGS{ICASSP_Graziani11,
|
| author |
= |
{Graziani, D. and Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{A new variational method for preserving point-like and curve-like singularities in 2d images}, |
| year |
= |
{2011}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Prague, Czech Republic}, |
| url |
= |
{http://hal.inria.fr/inria-00592603/fr/}, |
| keyword |
= |
{Convex optimization, nesterov scheme, laplacian operator} |
| } |
Abstract :
We propose a new variational method to restore point-like and curve-like singularities in 2-D images. As points and open curves are fine structures, they are difficult to restore by means of first order derivative operators computed in the noisy image. In this paper we propose to use the Laplacian operator of the observed intensity, since it becomes singular at points and curves. Then we propose to restore these singularities by introducing suitable regularization involving the l-1-norm of the Laplacian operator. Results are shown on synthetic an real data.
|
|
18 - Wavefront sensing for aberration modeling in fluorescence MACROscopy.
P. Pankajakshan et A. Dieterlen et G. Engler et Z. Kam et L. Blanc-Féraud et J. Zerubia et J.C. Olivo-Marin. Dans Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, USA, avril 2011.
Mots-clés : fluorescence MACROscopy , phase retrieval, field aberration.
@INPROCEEDINGS{PanjakshanISBI2011,
|
| author |
= |
{Pankajakshan, P. and Dieterlen, A. and Engler, G. and Kam, Z. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{Wavefront sensing for aberration modeling in fluorescence MACROscopy}, |
| year |
= |
{2011}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://hal.inria.fr/inria-00563988/en/}, |
| keyword |
= |
{fluorescence MACROscopy , phase retrieval, field aberration} |
| } |
Abstract :
In this paper, we present an approach to calculate the wavefront in
the back pupil plane of an objective in a fluorescent MACROscope.
We use the three-dimensional image of a fluorescent bead because it
contains potential pupil information in the ‘far’ out-of-focus planes
for sensing the wavefront at the back focal plane of the objective.
Wavefront sensing by phase retrieval technique is needed for several
reasons. Firstly, the point-spread function of the imaging system
can be calculated from the estimated pupil phase and used for image
restoration. Secondly, the aberrations in the optics of the objective
can be determined by studying this phase. Finally, the estimated
wavefront can be used to correct the aberrated optical path with-
out a wavefront sensor. In this paper, we estimate the wavefront of
a MACROscope optical system by using Bayesian inferencing and
derive the Gerchberg-Saxton algorithm as a special case. |
|
19 - Brain tumor vascular network segmentation from micro-tomography.
X. Descombes et F. Plouraboue et El Boustani Habdelhkim et Fonta Caroline et |LeDuc Geraldine et Serduc Raphael et Weitkamp Timm. Dans Internation Symposium of Biomedical Imaging (ISBI), Chicago, USA, avril 2011.
Mots-clés : Segmentation, Markov random field, Tomography, Brain, vascular network.
Copyright : IEEE
@INPROCEEDINGS{isbi11,
|
| author |
= |
{Descombes, X. and Plouraboue, F. and Boustani Habdelhkim, El and Caroline, Fonta and Geraldine, |LeDuc and Raphael, Serduc and Timm, Weitkamp}, |
| title |
= |
{Brain tumor vascular network segmentation from micro-tomography}, |
| year |
= |
{2011}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Internation Symposium of Biomedical Imaging (ISBI)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://dx.doi.org/10.1109/ISBI.2011.5872596}, |
| keyword |
= |
{Segmentation, Markov random field, Tomography, Brain, vascular network} |
| } |
Abstract :
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus characterized by their respective distribution of watershed region size interpreted as local vascular territories. |
|
haut de la page
4 Rapports de recherche et Rapports techniques |
1 - Classification of very high resolution SAR images of urban areas.
A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Rapport de recherche 7758, INRIA, octobre 2011.
Mots-clés : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet.
@TECHREPORT{RR-7758,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas}, |
| year |
= |
{2011}, |
| month |
= |
{octobre}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7758}, |
| url |
= |
{http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} |
| } |
Résumé :
Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en
vue d’améliorer la classification finale. |
Abstract :
In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results. |
|
2 - On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation.
V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Rapport de Recherche 7666, INRIA, juillet 2011.
Mots-clés : Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Radar a Ouverture Synthetique (SAR), Classification.
Copyright : INRIA/ARIANA
@TECHREPORT{RR-7666,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7666}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00605274/en/}, |
| keyword |
= |
{Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Radar a Ouverture Synthetique (SAR), Classification} |
| } |
Résumé :
L'estimation de paramètres de fonctions de densité de probabilité est une étape majeure dans le domaine du traitement statistique du signal et des images. Dans ce rapport, nous étudions les propriétés et les limites de l'estimation de paramètres par la méthode des cumulants logarithmiques (MoLC), qui est une alternative à la fois au maximum de vraisemblance (MV) classique et à la méthode des moments. Nous dérivons la condition générale suffisante de consistance forte de l'estimation par la méthode MoLC, qui représente une propriété asymptotique importante de tout estimateur statistique. Grâce à cela, nous démontrons la consistance forte de l'estimation par la méthode MoLC pour une sélection de familles de distributions particulièrement adaptées (mais non restreintes) au traitement d'images acquises par radar à synthèse d'ouverture (RSO). Nous dérivons ensuite les conditions analytiques d'applicabilité de la méthode MoLC à des échantillons générés qui suivent les lois des différentes familles de distribution de notre sélection. Enfin, nous testons la méthode MoLC sur des données synthétiques et réelles afin de comparer les différentes propriétés inhérentes aux différents types d'images, l'applicabilité de la méthode et les effets d'un nombre restreint d'échantillons. Nous avons, en particulier, considéré les distributions gamma généralisée et K. Comme exemple d'application, nous avons réalisé des classifications supervisées d'images médicales à ultrason ainsi que d'images de télédétection acquises par des capteurs RSO. Les résultats obtenus montrent que la méthode MoLC est une bonne alternative à la méthode des moments, bien qu'elle contienne certaines limitations. Elle est particulièrement utile lorsqu'une approche directe par MV n'est pas possible. |
Abstract :
Parameter estimation of probability density functions is one of the major steps in the mainframe of statistical image and signal processing. In this report we explore the properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition of strong consistency of MoLC estimates which represents an important asymptotic property of any statistical estimator. With its help we demonstrate the strong consistency of MoLC estimates for a selection of widely used distribution families originating (but not restricted to) synthetic aperture radar (SAR) image processing. We then derive the analytical conditions of applicability of MoLC to samples generated from several distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K family of distributions. Supervised image classification experiments are considered for medical ultrasound and remote sensing SAR imagery. The obtained results suggest MoLC to be a feasible yet not universally applicable alternative to MoM that can be considered when the direct ML approach turns out to be unfeasible. |
|
3 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model.
K. Kayabol et J. Zerubia. Rapport de Recherche 7700, INRIA, juillet 2011.
Mots-clés : High resolution SAR, Classification, Texture.
@TECHREPORT{Kayabol11,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{Unsupervised amplitude and texture based classification of SAR images with multinomial latent model}, |
| year |
= |
{2011}, |
| month |
= |
{juillet}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7700}, |
| url |
= |
{http://hal.archives-ouvertes.fr/hal-00612491/fr/}, |
| keyword |
= |
{High resolution SAR, Classification, Texture} |
| } |
Abstract :
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data.
|
|
4 - Restoration mehod for spatially variant blurred images.
S. Ben Hadj et L. Blanc-Féraud. Rapport de Recherche 7654, INRIA, juin 2011.
Mots-clés : Deconvolution, energy minimization, spatially-variant PSF, Variation totale.
@TECHREPORT{RR_SBH_11,
|
| author |
= |
{Ben Hadj, S. and Blanc-Féraud, L.}, |
| title |
= |
{Restoration mehod for spatially variant blurred images}, |
| year |
= |
{2011}, |
| month |
= |
{juin}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7654}, |
| url |
= |
{ http://hal.inria.fr/inria-00602650/fr/}, |
| keyword |
= |
{Deconvolution, energy minimization, spatially-variant PSF, Variation totale} |
| } |
|
haut de la page
2 Livres |
1 - Applications de la geometrie stochastique a l'analyse d'images.
X. Descombes et et al. Publ. Hermes, Ed. X. Descombes, 2011.
@BOOK{GeoStoFR,
|
| author |
= |
{Descombes, X. and al, et}, |
| title |
= |
{Applications de la geometrie stochastique a l'analyse d'images}, |
| year |
= |
{2011}, |
| publisher |
= |
{Hermes}, |
| edition |
= |
{X. Descombes}, |
| url |
= |
{http://www.lavoisier.fr/livre/mathematiques/applications-de-la-geometrie-stochastique-a-l-analyse-d-images/descombes/descriptif-9782746221451}, |
| keyword |
= |
{} |
| } |
|
2 - Stochastic geometry for image analysis.
X. Descombes et et al. Publ. Wiley/Iste, Ed. X. Descombes, 2011.
@BOOK{GeoStoEn,
|
| author |
= |
{Descombes, X. and al, et}, |
| title |
= |
{Stochastic geometry for image analysis}, |
| year |
= |
{2011}, |
| publisher |
= |
{Wiley/Iste}, |
| edition |
= |
{X. Descombes}, |
| url |
= |
{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1848212402.html}, |
| keyword |
= |
{} |
| } |
|
haut de la page
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Rapport d'activité 2011
bibtex
