|
|
Publications of 2004
Result of the query in the list of publications :
10 Articles |
1 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 108--125, September 2004.
Note : in Russian
@ARTICLE{DES04br,
|
| author |
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{Descombes, X. and Zhizhina, E.}, |
| title |
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{Applications of Gibbs fields methods to image processing problems}, |
| year |
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{2004}, |
| month |
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{Problems of Information Transmission}, |
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| pages |
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{108--125}, |
| note |
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{in Russian}, |
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{} |
| } |
|
2 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 279-295, September 2004.
Note : in English
@ARTICLE{DES04be,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
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{Applications of Gibbs fields methods to image processing problems}, |
| year |
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{2004}, |
| month |
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{September}, |
| journal |
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{Problems of Information Transmission}, |
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| number |
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{3}, |
| pages |
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{279-295}, |
| note |
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{in English}, |
| url |
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{http://link.springer.com/article/10.1023%2FB%3APRIT.0000044262.70555.5c}, |
| keyword |
= |
{} |
| } |
|
3 - Modelling SAR Images with a Generalization of the Rayleigh Distribution.
E.E. Kuruoglu and J. Zerubia. IEEE Trans. Image Processing, 13(4): pages 527 - 533, April 2004.
@ARTICLE{Kuruoglu03,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling SAR Images with a Generalization of the Rayleigh Distribution}, |
| year |
= |
{2004}, |
| month |
= |
{April}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pages |
= |
{527 - 533}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284389.pdf?tp=&arnumber=1284389&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
4 - An object based approach for detecting smallbrain lesions: application to Virchow-Robin spaces.
X. Descombes and F. Kruggel and G. Wollny and H.J. Gertz. IEEE Trans. Medical Imaging, 23(2): pages 246--255, February 2004.
@ARTICLE{DES04a,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Wollny, G. and Gertz, H.J.}, |
| title |
= |
{An object based approach for detecting smallbrain lesions: application to Virchow-Robin spaces}, |
| year |
= |
{2004}, |
| month |
= |
{February}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{23}, |
| number |
= |
{2}, |
| pages |
= |
{246--255}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/42/28264/01263613.pdf?tp=&arnumber=1263613&isnumber=28264}, |
| keyword |
= |
{} |
| } |
|
5 - A Gibbs point process for road extraction in remotely sensed images.
R. Stoica and X. Descombes and J. Zerubia. International Journal of Computer Vision, 57(2): pages 121--136, 2004.
@ARTICLE{STO04a,
|
| author |
= |
{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Gibbs point process for road extraction in remotely sensed images}, |
| year |
= |
{2004}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{57}, |
| number |
= |
{2}, |
| pages |
= |
{121--136}, |
| url |
= |
{http://www.springerlink.com/content/kr262t6084464n30/}, |
| keyword |
= |
{} |
| } |
|
6 - An adaptive Gaussian model for satellite image deblurring.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. IEEE Trans. Image Processing, 13(4), 2004.
@ARTICLE{JAL04a,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{An adaptive Gaussian model for satellite image deblurring}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284396.pdf?tp=&arnumber=1284396&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
7 - Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification.
G. Rellier and X. Descombes and F. Falzon and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 42(7): pages 1543--1551, 2004.
@ARTICLE{DES04c,
|
| author |
= |
{Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, |
| title |
= |
{Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{42}, |
| number |
= |
{7}, |
| pages |
= |
{1543--1551}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/29162/01315838.pdf?tp=&arnumber=1315838&isnumber=29162}, |
| keyword |
= |
{} |
| } |
|
8 - Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux.
M. Ortner and X. Descombes and J. Zerubia. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 173-174: pages 83--92, 2004.
@ARTICLE{ORT04a,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux}, |
| year |
= |
{2004}, |
| journal |
= |
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, |
| volume |
= |
{173-174}, |
| pages |
= |
{83--92}, |
| keyword |
= |
{} |
| } |
|
9 - Gamma-convergence of discrete functionals with nonconvex perturbation for image classification.
G. Aubert and L. Blanc-Féraud and R. March. SIAM Journal on Numerical Analysis, 42(3): pages 1128--1145, 2004.
@ARTICLE{BLA04,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{Gamma-convergence of discrete functionals with nonconvex perturbation for image classification}, |
| year |
= |
{2004}, |
| journal |
= |
{SIAM Journal on Numerical Analysis}, |
| volume |
= |
{42}, |
| number |
= |
{3}, |
| pages |
= |
{1128--1145}, |
| url |
= |
{http://epubs.siam.org/doi/abs/10.1137/S0036142902412336}, |
| keyword |
= |
{} |
| } |
|
10 - A nonlinear entropic variational model for image filtering.
A. Ben Hamza and H. Krim and J. Zerubia. EURASIP Journal on Applied Signal Processing, 16: pages 2408--2422, 2004.
@ARTICLE{JZHK04,
|
| author |
= |
{Ben Hamza, A. and Krim, H. and Zerubia, J.}, |
| title |
= |
{A nonlinear entropic variational model for image filtering}, |
| year |
= |
{2004}, |
| journal |
= |
{EURASIP Journal on Applied Signal Processing}, |
| volume |
= |
{16}, |
| pages |
= |
{2408--2422}, |
| url |
= |
{https://hal.inria.fr/hal-00784485/}, |
| keyword |
= |
{} |
| } |
|
 top of the page
4 PhD Thesis and Habilitations |
1 - Processus Ponctuels Marqués pour l'Extraction Automatique de Caricatures de Bâtiments à partir de Modèles Numériques d'Elévation.
M. Ortner. PhD Thesis, Universite de Nice Sophia Antipolis, October 2004.
Keywords : Marked point process, Object extraction, Buildings, Digital Elevation Model (DEM), RJMCMC, Stochastic geometry.
@PHDTHESIS{mortner_these,
|
| author |
= |
{Ortner, M.}, |
| title |
= |
{Processus Ponctuels Marqués pour l'Extraction Automatique de Caricatures de Bâtiments à partir de Modèles Numériques d'Elévation}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00189803}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/18/98/03/PDF/These_Ortner.pdf}, |
| keyword |
= |
{Marked point process, Object extraction, Buildings, Digital Elevation Model (DEM), RJMCMC, Stochastic geometry} |
| } |
Résumé :
Cette thèse se place dans un cadre de reconstruction urbaine et propose un corpus algorithmique pour extraire des formes simples sur les Modèles Numériques d'Elévation. Ce type de données décrit le relief d'une zone urbaine par une grille régulière de points à chacun desquels est associée une information de hauteur.
Les modèles utilisés reposent sur l'utilisation de processus ponctuels marqués. Il s'agit de variables aléatoires dont les réalisations sont des configurations d'objets géométriques. Ces modèles permettent d'introduire des contraintes sur la forme des objets recherchés dans une image ainsi qu'un terme de régularisation modélisé par des interactions entre les objets. Une énergie peut être associée aux configurations d'objets et la configuration minimisant cette énergie trouvée au moyen d'un recuit-simulé couplé à un échantillonneur de type Monte Carlo par Chaîne de Markov à sauts réversibles (RJMCMC).
Nous proposons quatre modèles pour extraire des caricatures de bâtiments à partir de descriptions altimétriques de zones urbaines denses. Chaque modèle est constitué par une forme d'objet, une énergie d'attache aux données et une énergie de régularisation. Les deux premiers modèles permettent d'extraire des formes simples (rectangles) en utilisant une contrainte d'homogénéité pour l'un et une détection des discontinuités pour l'autre. Le troisième modèle modélise les bâtiments par une forme polyhédrique. Le dernier modèle s'intéresse à l'apport d'une coopération entre des objets simples. Les algorithmes obtenus, automatiques, sont évalués sur des données réelles fournies par l'IGN (MNE Laser et optiques de différentes qualités). |
Abstract :
The context of this thesis is the reconstruction of urban areas from images. It proposes a set of algorithms for extracting simple shapes from Digital Elevation Models (DEM). DEMs describe the altimetry of an urban area by a grid of points, each of which has a height associated to it.
The proposed models are based on marked point processes. These mathematical objects are random variables whose realizations are configurations of geometrical shapes. Using these processes, we can introduce constraints on the shape of the objects to be detected in an image, and a regularizing term incorporating geometrical interactions between objects. An energy can be associated to each object configuration, and the global minima of this energy can then be found by applying simulated annealing to a Reversible Jump Monte Carlo Markov Chain sampler (RJMCMC).
We propose four different models for extracting the outlines of buildings from altimetric descriptions of dense urban areas. Each of these models is constructed from an object shape, a data energy, and a regularizing energy.
The first two models extract simple shapes (rectangles) using, respectively, a homogeneity constraint and discontinuity detection. The third model looks for three-dimensional polyhedral buildings. The last model uses cooperation between two types of objects, rectangles and segments.
The resulting algorithms are evaluated on real data provided by the French National Geographic Institute (a laser DEM and optical DEMs of differing quality). |
|
2 - Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués.
C. Lacoste. PhD Thesis, Universite de Nice Sophia Antipolis, September 2004.
Keywords : Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process.
@PHDTHESIS{lacoste_these,
|
| author |
= |
{Lacoste, C.}, |
| title |
= |
{Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00261397}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/26/13/97/PDF/THESE_CAROLINE_LACOSTE.pdf}, |
| keyword |
= |
{Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process} |
| } |
Résumé :
Cette thèse aborde le problème de l'extraction non supervisée des réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires et aériennes. Nous utilisons des processus objet, ou processus ponctuels marqués, comme modèles a priori. Ces modèles permettent de bénéficier de l'apport d'un cadre stochastique (robustesse au bruit, corpus algorithmique, etc.) tout en manipulant des contraintes géométriques fortes. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation.
Nous proposons tout d'abord une modélisation du réseau linéique par un processus dont les objets sont des segments interagissant entre eux. Le modèle a priori est construit de façon à exploiter au mieux la topologie du réseau recherché au travers de potentiels fondés sur la qualité de chaque interaction. Les propriétés radiométriques sont prises en compte dans un terme d'attache aux données fondé sur des mesures statistiques.
Nous étendons ensuite cette modélisation à des objets plus complexes. La manipulation de lignes brisées permet une extraction plus précise du réseau et améliore la détection des bifurcations.
Enfin, nous proposons une modélisation hiérarchique des réseaux hydrographiques dans laquelle les affluents d'un fleuve sont modélisés par un processus de lignes brisées dans le voisinage de ce fleuve.
Pour chacun des modèles, nous accélérons la convergence de l'algorithme MCMC par l'ajout de perturbations adaptées.
La pertinence de cette modélisation par processus objet est vérifiée sur des images satellitaires et aériennes, optiques et radar. |
Abstract :
This thesis addresses the problem of the unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. We use object processes, or marked point processes, as prior models. These models benefit from a stochastic framework (robustness w.r.t. noise, algorithms, etc.) while incorporating strong geometric constraints. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization.
We first propose to model line networks by a process whose objects are interacting line segments. The prior model is designed to exploit as fully as possible the topological properties of the network under consideration through potentials based on the quality of each interaction. The radiometric properties of the network are modeled using a data term based on statistical measures.
We then extend this model to more complex objects. The use of broken lines improves the detection of network junctions and increases the accuracy of the extracted network.
Finally, we propose a hierarchical model of hydrographic networks in which the tributaries of a given river are modeled by a process of broken lines in the neighborhood of this river. For each model, we accelerate convergence of the RJMCMC algorithm by using appropriate perturbations.
We show experimental results on aerial and satellite images (optical and radar data) to verify the relevance of the object process models. |
|
3 - Contribution à l'Analyse de Textures en Traitement d'Images par Méthodes Variationnelles et Equations aux Dérivées Partielles.
J.F. Aujol. PhD Thesis, Universite de Nice Sophia Antipolis, June 2004.
Keywords : Image decomposition, Classification, Restoration, Fonctional analysis, Bounded Variation Space, Sobolev space.
@PHDTHESIS{JFAujol,
|
| author |
= |
{Aujol, J.F.}, |
| title |
= |
{Contribution à l'Analyse de Textures en Traitement d'Images par Méthodes Variationnelles et Equations aux Dérivées Partielles}, |
| year |
= |
{2004}, |
| month |
= |
{June}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00006303}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/04/68/89/PDF/tel-00006303.pdf}, |
| keyword |
= |
{Image decomposition, Classification, Restoration, Fonctional analysis, Bounded Variation Space, Sobolev space} |
| } |
Résumé :
Cette thèse est un travail en mathématiques appliquées. Elle aborde quelques problèmes en analyse d'images et utilise des outils mathématiques spécifiques.
L'objectif des deux premières parties de cette thèse est de proposer un modèle pour décomposer une image f'en trois composantes : f=u+v+w. Notre approche repose sur l'utilisation d'espaces mathématiques adaptés à chaque composante: l'espace BV des fonctions à variations bornées pour u, un espace G'proche du dual de BV pour les textures, et un espace de Besov d'exposant négatif E'pour le bruit. Nous effectuons l'étude mathématique complète des différents modèles que nous proposons. Nous illustrons notre approche par de nombreux exemples.Dans la troisième et dernière partie de cette thèse, nous nous intéressons spécifiquement à la composante texturée. Nous proposons un algorithme de classification supervisée pour les images texturées. |
Abstract :
This Ph.D. thesis is a work in applied mathematics. It deals with image processing problems, and uses specific mathematical tools.
The aim of the two first parts is to propose a model for decomposing an image f'into three components : f=u+v+w. Our approach relies on the use of mathematical spaces adapted to each component : the space BV of functions with bounded variations for u, a space G'close to the dual space of BV for v, and a negative Besov space E'for w. We carry out the complete mathematical analysis of the different models we propose. We illustrate our approach with many numerical examples. In the third and last part, we only deal with the texture component of an image. We propose a supervised classification algorithm for textured images. |
|
4 - Méthodes stochastiques en analyse d'image : des champs de Markov aux processus ponctuels marqués.
X. Descombes. Habilitation à diriger des Recherches, Universite de Nice Sophia Antipolis, February 2004.
Keywords : Markov Fields, Stochastic geometry.
@PHDTHESIS{Xdescombes,
|
| author |
= |
{Descombes, X.}, |
| title |
= |
{Méthodes stochastiques en analyse d'image : des champs de Markov aux processus ponctuels marqués}, |
| year |
= |
{2004}, |
| month |
= |
{February}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| type |
= |
{Habilitation à diriger des Recherches}, |
| url |
= |
{https://hal.inria.fr/tel-00506084}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/HDRdescombes.pdf}, |
| keyword |
= |
{Markov Fields, Stochastic geometry} |
| } |
|
top of the page
18 Conference articles |
1 - Texture discrimination using multimodal wavelet packet subbands.
R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Bimodal, Adaptive, probabilistic, Wavelet packet, Texture.
@INPROCEEDINGS{cossu_icip04,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture discrimination using multimodal wavelet packet subbands}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icip.pdf}, |
| keyword |
= |
{Bimodal, Adaptive, probabilistic, Wavelet packet, Texture} |
| } |
Abstract :
The subband histograms of wavelet packet bases adapted to individual
texture classes often fail to display the leptokurtotic behaviour
shown by the standard wavelet coefcients of `natural'
images. While many subband histograms remain leptokurtotic
in adaptive bases, some subbands are Gaussian. Most interestingly,
however, some subbands show multimodal behaviour, with
no mode at zero. In this paper, we provide evidence for the existence
of these multimodal subbands and show that they correspond
to narrow frequency bands running throughout images of the texture.
They are thus closely linked to the texture's structure. As
such, they seem likely to possess superior descriptive and discriminative
power as compared to unimodal subbands. We demonstrate
this using both Brodatz and remote sensing images. |
|
2 - Segmentation of remote sensing images by supervised TS-MRF.
G. Poggi and G. Scarpa and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
@INPROCEEDINGS{poggi_icip04,
|
| author |
= |
{Poggi, G. and Scarpa, G. and Zerubia, J.}, |
| title |
= |
{Segmentation of remote sensing images by supervised TS-MRF}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1421441}, |
| keyword |
= |
{} |
| } |
|
3 - Gap closure in (road) networks using higher-order active contours.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Active contour, Gap closure, Higher-order, Shape, Road network.
@INPROCEEDINGS{Rochery04,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Gap closure in (road) networks using higher-order active contours}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip04.pdf}, |
| keyword |
= |
{Active contour, Gap closure, Higher-order, Shape, Road network} |
| } |
Abstract :
We present a new model for the extraction of networks from images in the presence of occlusions. Such occlusions cause gaps in the extracted network that need to be closed. Using higher-order active contours, which allow the incorporation of sophisticated geometric information, we introduce a new, non-local, `gap closure' force that causes pairs of network extremities that are close together to extend towards one another and join, thus closing the gap
between them. We demonstrate the benefits of the model using the problem of road network extraction, presenting results on aerial images. |
|
4 - Texture analysis using adaptative biorthogonal wavelet packets.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Singapore, October 2004.
Keywords : Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics.
@INPROCEEDINGS{Abhayratne_icip04,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using adaptative biorthogonal wavelet packets}, |
| year |
= |
{2004}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne04icip.pdf}, |
| keyword |
= |
{Adaptive, Wavelet packet, Biorthogonal, Texture, Statistics} |
| } |
Abstract :
We discuss the use of adaptive biorthogonal wavelet packet bases
in a probabilistic approach to texture analysis, thus combining the
advantages of biorthogonal wavelets (FIR, linear phase) with those
of a coherent texture model. The computation of the probability
uses both the primal and dual coefcients of the adapted biorthogonal
wavelet packet basis. The computation of the biorthogonal
wavelet packet coefcients is done using a lifting scheme, which
is very efficient. The model is applied to the classification of mosaics
of Brodatz textures, the results showing improvement over
the performance of the corresponding orthogonal wavelets. |
|
5 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
@INPROCEEDINGS{lacoste04b,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Unsupervised line network extraction from remotely sensed images by polyline process}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7079995}, |
| pdf |
= |
{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1608.pdf}, |
| keyword |
= |
{} |
| } |
|
6 - A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry.
M. Ortner and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
Note : this paper has received a Young Authors award
@INPROCEEDINGS{ortner04b,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Discontinuity detector for building extraction from Digital Elevation Models by stochastic geometry}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| note |
= |
{this paper has received a Young Authors award}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7079720}, |
| keyword |
= |
{} |
| } |
|
7 - Simultaneous structure and texture compact representation.
J.F. Aujol and B. Matei. In Proc. Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, September 2004.
@INPROCEEDINGS{jf_acivs,
|
| author |
= |
{Aujol, J.F. and Matei, B.}, |
| title |
= |
{Simultaneous structure and texture compact representation}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Brussels, Belgium}, |
| pdf |
= |
{http://www.math.u-bordeaux1.fr/~jaujol/PAPERS/acivscompression.pdf}, |
| keyword |
= |
{} |
| } |
|
8 - SAR amplitude probability density function estimation based on a generalized Gaussian scattering model.
G. Moser and J. Zerubia and S.B. Serpico. In Proc. SPIE Symposium on Remote Sensing, Maspalomas, Gran Canaria, Spain, September 2004.
@INPROCEEDINGS{moser2004a,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{SAR amplitude probability density function estimation based on a generalized Gaussian scattering model}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| address |
= |
{Maspalomas, Gran Canaria, Spain}, |
| url |
= |
{http://dx.doi.org/10.1117/12.567853}, |
| keyword |
= |
{} |
| } |
|
9 - Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families.
G. Moser and J. Zerubia and S.B. Serpico. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage , USA, September 2004.
@INPROCEEDINGS{moser2004b,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{Finite mixture models and stochastic EM for SAR amplitude probability density function estimation based on a dictionary of parametric families}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, |
| address |
= |
{Anchorage , USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1368708}, |
| keyword |
= |
{} |
| } |
|
10 - Tree Crown Extraction using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
Keywords : RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry.
@INPROCEEDINGS{perrin04a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Tree Crown Extraction using Marked Point Processes}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{University of Technology, Vienna, Austria}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.ps.gz}, |
| keyword |
= |
{RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry} |
| } |
Abstract :
In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process.
Then, we use a Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels.
Results are shown on aerial images of poplars provided by IFN. |
|
11 - Image Disocclusion Using a Probabilistic Gradient Orientation.
E. Villéger and G. Aubert and L. Blanc-Féraud. In Proc. International Conference on Pattern Recognition (ICPR), Cambridge, United Kingdom, August 2004.
@INPROCEEDINGS{Villeger04,
|
| author |
= |
{Villéger, E. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Image Disocclusion Using a Probabilistic Gradient Orientation}, |
| year |
= |
{2004}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Cambridge, United Kingdom}, |
| pdf |
= |
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1334034}, |
| keyword |
= |
{} |
| } |
|
12 - A Reversible Jump MCMC sampler for building detection in image processing.
M. Ortner and X. Descombes and J. Zerubia. In Monte Carlo Methods and Quasi-Monte Carlo Methods, series Special Se, Juan les Pins (France), June 2004.
@INPROCEEDINGS{mcmcqmc,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Reversible Jump MCMC sampler for building detection in image processing}, |
| year |
= |
{2004}, |
| month |
= |
{June}, |
| booktitle |
= |
{Monte Carlo Methods and Quasi-Monte Carlo Methods}, |
| series |
= |
{Special Se}, |
| address |
= |
{Juan les Pins (France)}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-31186-6_23}, |
| keyword |
= |
{} |
| } |
|
13 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, May 2004.
@INPROCEEDINGS{lacoste04a,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Montreal, Quebec, Canada}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1326607}, |
| keyword |
= |
{} |
| } |
|
14 - A $l^1$-unified variational framework for image restoration.
J. Bect and L. Blanc-Féraud and G. Aubert and A. Chambolle. In Proc. European Conference on Computer Vision (ECCV), Vol. LNCS 3024, pages 1--13, Ed. T. Pajdla and J. Matas, Publ. Springer, Prague, Czech Republic, May 2004.
@INPROCEEDINGS{eccv04,
|
| author |
= |
{Bect, J. and Blanc-Féraud, L. and Aubert, G. and Chambolle, A.}, |
| title |
= |
{A $l^1$-unified variational framework for image restoration}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
| volume |
= |
{LNCS 3024}, |
| pages |
= |
{1--13}, |
| editor |
= |
{T. Pajdla and J. Matas}, |
| publisher |
= |
{Springer}, |
| address |
= |
{Prague, Czech Republic}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-24673-2_1}, |
| keyword |
= |
{} |
| } |
|
15 - Deconvolution in confocal microscopy with total variation regularization.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB), pages 117--120, May 2004.
@INPROCEEDINGS{Dey04b,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Deconvolution in confocal microscopy with total variation regularization}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. French-Danish Workshop on Spatial Statistics and Image Analysis in Biology (SSIAB)}, |
| pages |
= |
{117--120}, |
| url |
= |
{http://www3.jouy.inra.fr/miaj/public/imaste/ssiab2004/program/abw92/}, |
| keyword |
= |
{} |
| } |
|
16 - Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients.
R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Canada, May 2004.
Keywords : Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics.
@INPROCEEDINGS{cossu04a,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptative wavelet packet coefficients}, |
| year |
= |
{2004}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Montreal, Canada}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icassp.pdf}, |
| keyword |
= |
{Bimodal, Adaptive, Wavelet packet, Texture, Gaussian mixture, Statistics} |
| } |
Abstract :
Although subband histograms of the wavelet coefficients of
natural images possess a characteristic leptokurtotic form,
this is no longer true for wavelet packet bases adapted to
a given texture. Instead, three types of subband statistics
are observed: Gaussian, leptokurtotic, and interestingly, in
some subbands, multimodal histograms. These subbands
are closely linked to the structure of the texture, and guarantee
that the most probable image is not flat. Motivated by
these observations, we propose a probabilistic model that
takes them into account. Adaptive wavelet packet subbands
are modelled as Gaussian, generalized Gaussian, or a constrained
Gaussian mixture. We use a Bayesian methodology,
finding MAP estimates for the adaptive basis, for subband
model selection, and for subband model parameters.
Results confirm the effectiveness of the proposed approach,
and highlight the importance of multimodal subbands for
texture discrimination and modelling. |
|
17 - A deconvolution method for confocal microscopy with total variation regularization.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, USA, April 2004.
Keywords : 3D confocal microscopy, Poisson deconvolution, total variation regularization.
@INPROCEEDINGS{Dey04a,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{A deconvolution method for confocal microscopy with total variation regularization}, |
| year |
= |
{2004}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| address |
= |
{Arlington, USA}, |
| pdf |
= |
{http://dx.doi.org/10.1109/ISBI.2004.1398765}, |
| keyword |
= |
{3D confocal microscopy, Poisson deconvolution, total variation regularization} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this method tends to amplify noise if used without regularizing constraint. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on total variation, whose smoothing avoids oscillations while preserving edges. We show on simulated images that this constraint improves the deconvolution result both visually and using quantitative measures. |
|
18 - Marked Point Process in Image Analysis : from Context to Geometry.
X. Descombes and F. Kruggel and C. Lacoste and M. Ortner and G. Perrin and J. Zerubia. In International Conference on Spatial Point Process Modelling and its Application (SPPA), Castellon, Spain, 2004.
Keywords : RJMCMC, Object extraction, Marked point process, Stochastic geometry.
@INPROCEEDINGS{geostoch04a,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Lacoste, C. and Ortner, M. and Perrin, G. and Zerubia, J.}, |
| title |
= |
{Marked Point Process in Image Analysis : from Context to Geometry}, |
| year |
= |
{2004}, |
| booktitle |
= |
{International Conference on Spatial Point Process Modelling and its Application (SPPA)}, |
| address |
= |
{Castellon, Spain}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.ps.gz}, |
| keyword |
= |
{RJMCMC, Object extraction, Marked point process, Stochastic geometry} |
| } |
Abstract :
We consider the marked point process framework as a natural extension of the Markov random field approach in image analysis. We consider a general model defined by its density allowing us to consider some geometrical constraints on objects and between objects in feature extraction problems. Some examples are derived for small brain lesions detection from MR Images, road network, tree crown and building extraction from remotely sensed images. The results obtained on real data show the relevance of the proposal approach. |
|
top of the page
9 Technical and Research Reports |
1 - Détection de Feux de Forêt par Analyse Statistique de la Radiométrie d'Images Satellitaires.
F. Lafarge and X. Descombes and J. Zerubia. Research Report 5369, INRIA, France, December 2004.
Keywords : Forest fires, Gaussian Field, Rare event.
@TECHREPORT{5369,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Détection de Feux de Forêt par Analyse Statistique de la Radiométrie d'Images Satellitaires}, |
| year |
= |
{2004}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5369}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070634}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70634/filename/RR-5369.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/06/34/PS/RR-5369.ps}, |
| keyword |
= |
{Forest fires, Gaussian Field, Rare event} |
| } |
Résumé :
Nous proposons, dans ce rapport, une méthode de détection des feux de forêt par imagerie satellitaire fondée sur la théorie des champs aléatoires. L'idée consiste à modéliser l'image par une réalisation d'un champ gaussien afin d'en extraire, par une analyse statistique, les éléments étrangers pouvant correspondre aux feux.
Le canal IRT (InfraRouge Thermique) contient des longueurs d'onde particulièrement sensibles à l'émission de chaleur. L'intensité d'un pixel d'une image IRT est donc d'autant plus forte que la température de la zone associée à ce pixel est élevée. Les feux de forêt peuvent alors être caractérisés par des pics d'intensité sur ce type d'images. Nous proposons une méthode de classification non supervisée et automatique fondée sur la théorie des champs gaussiens. Pour ce faire, nous modélisons dans un premier temps l'image par une réalisation d'un champ gaussien. Les zones de feux, minoritaires et de fortes intensités sont considérées comme des éléments étrangers à ce champ : ce sont des évènements rares. Ensuite, par une analyse statistique, nous déterminons un jeu de probabilités définissant, pour une zone donnée de l'image, un degré d'appartenance au champ gaussien, et par complémentarité aux zones potentiellement en feux. |
Abstract :
We present in this report a method for forest fire detection in satellite images based on random field theory. The idea is to model the image as a realization of a gaussian field in order to extract the rare events, which are potential fires, by a statistical analysis.
The TIR (Thermical InfraRed) channel has a wavelength sensitive to the emission of heat : the higher the heat of a area, the higher the intensity of the corresponding pixel of the image. Then a forest fire can be characterized by peak intensity in TIR images. We present an fully automatic unsupervised classification method based on Gaussian field theory. First we model the image as a realization of a Gaussian field. The fire areas, which have high intensity and are supposed to be a minority, are considered as foreign elements of that field : they are rare events. Then we determine by a statistical analysis a set of probabilities which characterizes the degree of belonging to the Gaussian field of a small area of the image. So, we estimate the probability that the area is a potential fire. |
|
2 - Noyaux Texturaux pour les Problèmes de Classification par SVM en Télédétection.
F. Lafarge and X. Descombes and J. Zerubia. Research Report 5370, INRIA, France, December 2004.
Keywords : Support Vector Machines, Classification, Forest fires, Urban areas, Learning base, Markov Fields.
@TECHREPORT{5370,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Noyaux Texturaux pour les Problèmes de Classification par SVM en Télédétection}, |
| year |
= |
{2004}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5370}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070633}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70633/filename/RR-5370.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/06/33/PS/RR-5370.ps}, |
| keyword |
= |
{Support Vector Machines, Classification, Forest fires, Urban areas, Learning base, Markov Fields} |
| } |
Résumé :
Nous détaillons dans ce rapport la construction de deux noyaux texturaux s'utilisant dans les problèmes de classification par «Support Vector Machines» en télédétection. Les SVM constituent une méthode de classification supervisée particulièrement bien adaptée pour traiter des données de grande dimension telles que les images satellitaires. Par cette méthode, nous souhaitons réaliser l'apprentissage de paramètres qui permettent la différenciation entre deux ensembles de pixels connexes non-identiques. Nous travaillons pour cela sur des fonctions noyaux, fonctions caractérisant une certaine similarité entre deux données. Dans notre cas, cette similarité sera fondée à la fois sur une notion radiométrique et sur une notion texturale. La principale difficulté rencontrée dans cette étude réside dans l'élaboration de paramètres texturaux pertinents qui modélisent au mieux l'homogénéité d'un ensemble de pixels connexes. Nous appliquons les noyaux proposés à deux problèmes de télédétection: la détection de feux de forêt et la détection de zones urbaines à partir d'images satellitaires haute résolusion. |
Abstract :
We present in this report two textural kernels for «Support Vector Machines» classification applied to remote sensing problems. SVMs constitute a method of supervised classification well adapted to deal with data of high dimension, such as images. We would like to learn parameters which allow the differentiation between two sets of connected pixels. We also introduce kernel functions which characterize a notion of similarity between two pieces of data. In our case this similarity is based on a radiometric charateristic and a textural characteristic. The main difficulty is to elaborate textural parameters which are pertinent and characterize as well as possible the homogeneity of a set of connected pixels. We apply this method to remote sensing problems : the detection of forest fires and the extraction of urban areas in high resolution satellite images. |
|
3 - Detecting Codimension-two Objects in an Image with Ginzburg-Landau Models.
G. Aubert and J.F. Aujol and L. Blanc-Féraud. Research Report 5254, INRIA, France, July 2004.
Keywords : Ginzburg-Landau model, Biological images, Segmentation, Partial differential equation.
@TECHREPORT{5254,
|
| author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
| title |
= |
{Detecting Codimension-two Objects in an Image with Ginzburg-Landau Models}, |
| year |
= |
{2004}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5254}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070744}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70744/filename/RR-5254.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/07/44/PS/RR-5254.ps}, |
| keyword |
= |
{Ginzburg-Landau model, Biological images, Segmentation, Partial differential equation} |
| } |
Résumé :
Dans cet article, nous proposons a nouveau modèle mathématique pour détecter dans une image les singularités de codimension supérieure ou égale à deux. Cela signifie que nous voulons détecter des points dans des images 2-D, ou des points et des courbes dans des images 3-D. Nous nous inspirons des modèles de Ginzburg-Landau (GL). Ces derniers se sont révélés efficace pour modéliser de nombreux phénomènes physiques. Nous introduisons le modèle, nous énonçons ses propriétés mathématiques, et nous donnons des résultats expérimentaux illustrant les performances du modèle. |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its mathematical properties and give some experimental results demonstrating its capability. |
|
4 - 3D Microscopy Deconvolution using Richardson-Lucy Algorithm with Total Variation Regularization.
N. Dey and L. Blanc-Féraud and C. Zimmer and P. Roux and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Research Report 5272, INRIA, France, July 2004.
Keywords : Confocal microscopy, Deconvolution, Impulse answer, Total variation.
@TECHREPORT{5272,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Roux, P. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{3D Microscopy Deconvolution using Richardson-Lucy Algorithm with Total Variation Regularization}, |
| year |
= |
{2004}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5272}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070726/fr/}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70726/filename/RR-5272.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/07/26/PS/RR-5272.ps}, |
| keyword |
= |
{Confocal microscopy, Deconvolution, Impulse answer, Total variation} |
| } |
Résumé :
La microscopie confocale (Confocal laser scanning microscopy ou microscopie confocale à balayage laser) est une méthode puissante de plus en plus populaire pour l'imagerie 3D de spécimens biologiques. Malheureusement, les images acquises sont dégradées non seulement par du flou dû à la lumière provenant de zones du spécimen non focalisées, mais aussi par un bruit de Poisson dû à la détection, qui se fait à faible flux de photons. Plusieurs méthodes de déconvolution ont été proposées pour réduire ces dégradations, avec en particulier l'algorithme itératif de Richardson-Lucy, qui calcule un maximum de vraisemblance adapté à une statistique poissonienne. Mais cet algorithme utilisé comme tel ne converge pas nécessairement vers une solution adaptée, car il tend à amplifier le bruit. Si par contre on l'utilise avec une contrainte de régularisation (connaissance a priori sur l'objet que l'on cherche à restaurer, par exemple), Richardson-Lucy régularisé converge toujours vers une solution adaptée, sans amplification du bruit. Nous proposons ici de combiner l'algorithme de Richardson-Lucy avec une contrainte de régularisation basée sur la Variation Totale, dont l'effet d'adoucissement permet d'éviter les oscillations d'intensité tout en préservant les bords des objets. Nous montrons sur des images synthétiques et sur des images réelles que cette contrainte de régularisation améliore les résultats de la déconvolution à la fois qualitativement et quantitativement. Nous comparons plusieurs méthodes de déconvolution bien connues à la méthode que nous proposons, comme Richardson-Lucy standard (pas de régularisation), Richardson-Lucy régularisé avec Tikhonov-Miller, et un algorithme basé sur la descente de gradients (sous l'hypothèse d'un bruit additif gaussien). |
Abstract :
Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. However this algorithm does not necessarily converge to a suitable solution, as it tends to amplify noise. If it is used with a regularizing constraint (some prior knowledge on the data), Richardson-Lucy regularized with a well-chosen constraint, always converges to a suitable solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularizing constraint based on Total Variation, whose smoothing avoids oscillations while preserving object edges. We show on simulated and real images that this constraint improves the deconvolution results both visually and using quantitative measures. We compare several well-known deconvolution methods to the proposed method, such as standard Richardson-Lucy (no regularization), Richardson-Lucy with Tikhonov-Miller regularization, and an additive gradient-based algorithm. |
|
5 - Dual Norms and Image Decomposition Models.
J.F. Aujol and A. Chambolle. Research Report 5130, INRIA, France, March 2004.
Keywords : Total variation, Bounded Variation Space, Image decomposition.
@TECHREPORT{5130,
|
| author |
= |
{Aujol, J.F. and Chambolle, A.}, |
| title |
= |
{Dual Norms and Image Decomposition Models}, |
| year |
= |
{2004}, |
| month |
= |
{March}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5130}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071453}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71453/filename/RR-5130.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/14/53/PS/RR-5130.ps}, |
| keyword |
= |
{Total variation, Bounded Variation Space, Image decomposition} |
| } |
Résumé :
Inspiré par [16], de nombreux modèles de décomposition d'images en une composante géométrique et une composante texturée ont été proposés en traitement d'images. Dans de telles approches, les normes d'espaces de Sobolev d'exposant négatif ont paru intéressantes pour modéliser les éléments oscillants. Dans ce papier, nous comparons les propriétés de différentes normes qui sont duales de normes de Sobolev ou de Besov. Nous proposons ensuite un modèle de décomposition qui sépare une image en deux composantes, une première contenant les structures de l'image, une seconde les textures de l'image, et une troisième le bruit. Notre modèle de décomposition repose sur l'utilisation de trois semi-normes différentes: la variation totale pour la composante géométrique, une norme de Sobolev négative pour la texture, et une norme de Besov négative pour le bruit. Nous illustrons notre étude par des exemples numériques. |
Abstract :
Following [16], decomposition models into a geometrical component and a textured component have recently been proposed in image processing. In such approaches, negative Sobolev norms have seemed to be useful to modelize oscillating patterns. In this paper, we compare the properties of various norms that are dual of Sobolev or Besov norms. We then propose a decomposition model which splits an image into three components: a first one containing the structure of the image, a second one the texture of the image, and a third one the noise. Our decomposition model relies on the use of three different semi-norms: the total variation for the geometrical componant, a negative Sobolev norm for the texture, and a negative Besov norm for the noise. We illustrate our study with numerical examples. |
|
6 - SAR Amplitude Probability Density Function Estimation based on a Generalized Gaussian Scattering Model.
G. Moser and J. Zerubia and S.B. Serpico. Research Report 5153, INRIA, France, March 2004.
Keywords : Synthetic Aperture Radar (SAR), Generalised Gaussians.
@TECHREPORT{5153,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{SAR Amplitude Probability Density Function Estimation based on a Generalized Gaussian Scattering Model}, |
| year |
= |
{2004}, |
| month |
= |
{March}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5153}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071430}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71430/filename/RR-5153.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/14/30/PS/RR-5153.ps}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR), Generalised Gaussians} |
| } |
Résumé :
En télédetection, un problème important est celui de développer des modèles précis pour representer les statistiques des intensités des pixels. En ce qui concerne les données du type Radar à Synthèse d'Ouverture (RSO), cette modélisation constitue un point capital pour la classification ou le débruitage d'une image, par exemple. Dans ce rapport de recherche, une nouvelle méthode d'estimation paramétrique pour les amplitudes d'images RSO est proposée. Elle tient compte de la nature physique des phénomènes de diffusion qui générent une image RSO en adoptant une modèle de gaussiennes generalisées pour les phénomènes de rétrodiffusion. Une expression, sous forme explicite, de la densité de probabilité de l'amplitude est obtenue et un algorithme spécifique d'estimation des paramètres est proposé afin de pouvoir utiliser le modèle proposé. Une mèthode récente fondée sur les «logs-cumulants» est appliquée, dérivant de l'utilisation d'une transformée de Mellin (à la place de la transformée de Fourier usuelle) dans le calcul des fonctions caractéristiques et de la généralisation des concepts de moment et de cumulant correspondante. Les estimées obtenues par la mèthode des log-cumulants pour le modèle d'amplitude fondé sur des gaussiennes généralisées se révelent être calculables numériquement et également consistantes. Dans ce rapport de recherche, l'approche paramètrique proposée est validée sur diverses images radar RSO (ERS, XSAR, ESAR et des radar aéroportés). Les résultats expérimentaux montrent que la mèthode proposée modèlise mieux la densité de probabilité de l'amplitude que beaucoup de modèles paramétriques proposés précédemment pour les phénomènes de rétrodiffusion. |
Abstract :
In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on Synthetic Aperture Radar (SAR) data, this modelling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In the present report, an innovative parametric estimation methodology for SAR amplitude data is proposed, which takes into account the physical nature of the scattering phenomena generating a SAR image by adopting a generalized Gaussian (GG) model for the backscattering phenomena. A closed form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed «method-of-log-cumulants» (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions, and from the corresponding generalization of the concepts of moment and of cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also proved to be consistent. The proposed parametric approach is validated using several real ERS-1, XSAR, ESAR and airborne SAR images and the experimental results prove that the method models the amplitude probability density function better than several previously proposed parametric models for the backscattering phenomena. |
|
7 - Dictionary-based Stochastic Expectation-Maximization for SAR amplitude probability density function estimation.
G. Moser and J. Zerubia and S.B. Serpico. Research Report 5154, INRIA, France, March 2004.
Keywords : Synthetic Aperture Radar (SAR), Stochastic EM (SEM), Finite mixing model.
@TECHREPORT{5154,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{Dictionary-based Stochastic Expectation-Maximization for SAR amplitude probability density function estimation}, |
| year |
= |
{2004}, |
| month |
= |
{March}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5154}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071429}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71429/filename/RR-5154.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/14/29/PS/RR-5154.ps}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR), Stochastic EM (SEM), Finite mixing model} |
| } |
Résumé :
En télédetection, un problème vital est le besoin de développer des modèles précis pour représenter les statistiques des intensités des images. Dans ce rapport de recherche, nous traitons le problème de l'estimation de la densité de probabilité de l'amplitude d'une image de type Radar à Synthèse d'Ouverture (RSO). Plusieurs modèles théoriques ou heuristiques, ultilisés pour représenter l'amplitude d'un signal du type RSO, ont été proposés dans la littérature et ce sont révelés être efficaces pour différentes types de classes dans le contexte des cartes d'occupation des sols, rendant ainsi difficile le choix d'une seule densité de probabilité paramétrique. Dans ce rapport de recherche, un algorithme d'estimation innovant est proposé, se fondant sur un modèle de mélange fini pour la densité de probabilité de l'amplitude, les diverses composantes du mélange appartenant à un dictionnaire specifique. La mèthode proposée dans ce rapport intégre, de fa on automatique, les procédures de sélection d'un modèle optimal pour chaque composante, d'estimation de paramètres et d'optimisation du nombre de composantes, en combinant un algorithme EM stochastique et la méthode des logs-cumulants pour l'estimation de la densité de probabilité paramètrique. Des resultats expérimentaux sur plusieurs images RSO réelles sont présentés, montrant ainsi que la mèthode proposée est suffisamment précise pour modéliser les statistiques du signal d'amplitude radar de type RSO. |
Abstract :
In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. In the current research report, we address the problem of parametric probability density function (PDF) estimation in the context of Synthetic Aperture Radar (SAR) amplitude data analysis. Specifically, several theoretical and heuristic models for the PDFs of SAR data have been proposed in the literature, and have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal SAR parametric PDF a hard task. In thia report, an innovative estimation algorithm is proposed, which addresses this problem by adopting a finite mixture model (FMM) for the amplitude PDF, with mixture components belonging to a given dictionary of SAR-specific PDFs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components, by combining the Stochastic Expectation Maximization (SEM) iterative methodology and the recently proposed «method-of-log-cumulants» (MoLC) for parametric PDF estimation for non-negative random variables. Experimental results on several real SAR images are presented, showing the proposed method is accurately modelling the statistics of SAR amplitude data. |
|
8 - Models of the Unimodal and Multimodal Statistics of Adaptive Wavelet Packet Coefficients.
R. Cossu and I. H. Jermyn and K. Brady and J. Zerubia. Research Report 5122, INRIA, France, February 2004.
Keywords : Wavelet packet, Texture.
@TECHREPORT{5122,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Brady, K. and Zerubia, J.}, |
| title |
= |
{Models of the Unimodal and Multimodal Statistics of Adaptive Wavelet Packet Coefficients}, |
| year |
= |
{2004}, |
| month |
= |
{February}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5122}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071461}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71461/filename/RR-5122.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/14/61/PS/RR-5122.ps}, |
| keyword |
= |
{Wavelet packet, Texture} |
| } |
Résumé :
De récents travaux ont montré que bien que les histogrammes de sous-bandes pour les coefficients d'ondelettes standards ont une forme de gaussienne généralisée, ce n'est plus vrai pour les bases de paquets d'ondelettes adaptés à une certaine texture. Trois types de statistiques sont alors observés pour les sous-bandes: gaussienne, gaussienne generalisée et dans certaines sous-bandes des histogrammes multimodaux sans mode en zéro. Dans ce rapport, nous démontrons que ces sous-bandes sont étroitement liées à la structure de la texture et sont ainsi primordiales dans les applications dans lesquelles la texture joue un rôle important. Fort de ces observations, nous étendons l'approche de modélisation de textures proposée par en incluant ces sous-bandes. Nous modifions l'hypothèse gaussienne pour inclure les gaussiennes généralisées et les mixtures de gaussiennes contraintes. Nous utilisons une méthodologie bayésienne, définissant des estimateurs MAP pour la base adaptative, pour la sélection du modèle de la sous-bande et pour les paramètres de ce modèle. Les résultats confirment l'efficacité de la méthode proposée et soulignent l'importance des sous-bandes multimodales pour la discrimination et la modélisation de textures. |
Abstract :
In recent work, it was noted that although the subband histograms for standard wavelet coefficients take on a generalized Gaussian form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, generalized Gaussian, and most interestingly, in some subbands, multimodal histograms with no mode at zero. As will be demonstrated in this report, these latter subbands are closely linked to the structure of the texture, and are thus likely to be important for many applications in which texture plays a role. Motivated by these observations, we extend the approach to texture modelling proposed by to include these subbands. We relax the Gaussian assumption to include generalized Gaussians and constrained Gaussian mixtures. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling. |
|
9 - Structure and Texture Compression.
J.F. Aujol and B. Matei. Research Report 5076, INRIA, France, January 2004.
Keywords : Bounded Variation Space, Image decomposition, Texture, Structure.
@TECHREPORT{5076,
|
| author |
= |
{Aujol, J.F. and Matei, B.}, |
| title |
= |
{Structure and Texture Compression}, |
| year |
= |
{2004}, |
| month |
= |
{January}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5076}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00071507}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/71507/filename/RR-5076.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/15/07/PS/RR-5076.ps}, |
| keyword |
= |
{Bounded Variation Space, Image decomposition, Texture, Structure} |
| } |
Résumé :
Dans ce papier, nous nous intéressons au problème de la compression d'image. Les ondelettes se sont révélées être un outil particulièremment efficace . Récemment, de nombreux algorithmes ont été proposés pour amméliorer la compression par ondelettes en essayant de prendre en compte les strucutres présentes dans l'image. De telles méthodes se révèlents très efficaces pour les images géométriques. Nous construisons un algorithme de compression d'images qui prend en compte la géométrie de l'image tout en étant capable d'être performant sur des images contenant à la fois des structures et des textures. Pour cela, nous utilisons un algorithme de décomposition d'image récemment introduit dans . Cet algorithme permet de séparer une image en deux composantes, une première composante contenant l'information géométrique de l'image, et une deuxième contenant les éléments oscillants de l'image. L'idée de notre méthode de compression est la suivante. Nous commen ons par décomposer l'image à compresser en sa partie géométrique et sa partie oscillante. Nous effectuons ensuite la compression de la partie géométrique à l'aide de l'algorithme introduit dans , ce dernier étant particulièrement bien adapté pour la compression des structures d'une image. Pour la partie oscillante de l'image, nous utilisons l'algorithme classique de compression par ondelettes biorthogonales. sur les zones régulières d'une image). l'image. Notre nouvel algorithme de compression s'avère plus performant que la méthode classique par ondelettes biorthogonales. meilleurs à la fois en PSNR, et aussi visuellement (les bords sont plus précis et les textures sont mieux conservées). |
Abstract :
In this paper, we tackle the problem of image compression. During the last past years, many algorithms have been proposed to take advantage of the geometry of the image. We intend here to propose a new compression algorithm which would take into account the structures in the image, and which would be powerful even when the original image has some textured areas. To this end, we first split our image into two components, a first one containing the structures of the image, and a second one the oscillating patterns. We then perform the compression of each component separately. Our final compressed image is the sum of these two compressed components. This new compression algorithm outperforms the standard biorthogonal wavelets compession. |
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