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Publications de 2002
Résultat de la recherche dans la liste des publications :
4 Articles |
1 - Marked Point Processes in Image Analysis.
X. Descombes et J. Zerubia. IEEE Signal Processing Magazine, 19(5): pages 77-84, septembre 2002.
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2 - Extension of phase correlation to subpixel registration.
H. Foroosh et J. Zerubia et M. Berthod. IEEE Trans. on Image Processing, 11(3): pages 188 - 200, mars 2002.
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{Extension of phase correlation to subpixel registration}, |
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{IEEE Trans. on Image Processing}, |
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3 - Local registration and deformation of a road cartographic database on a SPOT Satellite Image.
G. Rellier et X. Descombes et J. Zerubia. Pattern Recognition, 35(10), 2002.
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{Local registration and deformation of a road cartographic database on a SPOT Satellite Image}, |
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{Pattern Recognition}, |
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4 - Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method.
A. Jalobeanu et L. Blanc-Féraud et J. Zerubia. Pattern Recognition, 35(2): pages 341--352, 2002.
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{Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method}, |
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2 Thèses de Doctorat et Habilitations |
1 - Segmentation d'images d'observation de la Terre par des techniques de géométrie probabiliste.
S. Drot. Thèse de Doctorat, Universite de Nice Sophia Antipolis, décembre 2002.
Note : papier (tu-0758)
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{Segmentation d'images d'observation de la Terre par des techniques de géométrie probabiliste}, |
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{2002}, |
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{décembre}, |
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{Universite de Nice Sophia Antipolis}, |
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{papier (tu-0758)}, |
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2 - Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes.
G. Rellier. Thèse de Doctorat, Universite de Nice Sophia Antipolis, novembre 2002.
Mots-clés : Imagerie hyperspectrale, Texture, Classification, Champs de Markov.
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{2002}, |
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{novembre}, |
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{Universite de Nice Sophia Antipolis}, |
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{https://hal.inria.fr/tel-00505898}, |
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{Imagerie hyperspectrale, Texture, Classification, Champs de Markov} |
| } |
Résumé :
Dans cette thèse, on aborde le problème de l'analyse de texture pour l'étude des zones urbaines. La texture est une notion spatiale désignant ce qui, en dehors de la couleur ou du niveau de gris, caractérise l'homogénéité visuelle d'une zone donnée d'une image. Le but de cette étude est d'établir un modèle qui permette une analyse de texture prenant en compte conjointement l'aspect spatial et l'aspect spectral, à partir d'images hyperspectrales. Ces images sont caractérisées par un nombre de canaux largement supérieur à celui des images multispectrales classiques. On désire tirer parti de l'information spectrale pour améliorer l'analyse spatiale. Les textures sont modélisées par un champ de Markov gaussien vectoriel, qui permet de prendre en compte les relations spatiales entre pixels, mais aussi les relations inter-bandes à l'intérieur d'un même pixel. Ce champ est adapté aux images hyperspectrales par une simplification évitant l'apparition de problèmes d'estimation statistique dans des espaces de grande dimension. Dans le but d'éviter ces problèmes, on effectue également une réduction de dimension des données grâce à un algorithme de poursuite de projection. Cet algorithme permet de déterminer un sous-espace de projection dans lequel une grandeur appelée indice de projection est optimisée. L'indice de projection est défini par rapport à la modélisation de texture proposée, de manière à ce que le sous-espace optimal maximise la distance entre les classes prédéfinies, dans le cadre de la classification. La méthode d'analyse de texture est testée dans le cadre d'une classification supervisée. Pour ce faire, on met au point deux algorithmes que l'on compare avec des algorithmes classiques utilisant ou non l'information de texture. Des tests sont réalisés sur des images hyperspectrales AVIRIS. |
Abstract :
In this work, we investigate the problem of texture analysis of urban areas. Texture is a spatial concept that refers to the visual homogeneity characteristics of an image, not taking into account color or grey level. The aim of this research is to define a model which allows a joint spectral and spatial analysis of texture, and then to apply this model to hyperspectral images. These images many more bands than classical multispectral images. We intend to make use of spectral information and improve simple spatial analysis. Textures are modeled by a vectorial Gauss-Markov random field, which allows us to take into account the spatial interactions between pixels as well as inter-band relationships for a single pixel. This field has been adapted to hyperspectral images by a simplification which avoids statistical estimation problems common to high dimensional spaces. In order to avoid these problems, we also reduce the dimensionality of the data, using a projection pursuit algorithm. This algorithm determines a projection subspace in which an index, called projection index, is optimized. This index is defined in relation to the proposed texture model so that, when a classification is being carried out, the optimal subspace maximizes the distance between predefined training samples. This texture analysis method is tested within a supervised classification framework. For this purpose, we propose two classification algorithms that we compare to two classical algorithms, one which uses texture information and one which does not. Tests are carried out on AVIRIS hyperspectral images. |
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12 Articles de conférence |
1 - Segmentation of Pathological Features in MRI Brain Datasets.
F. Kruggel et C. Chalopin et X. Descombes et V. Kovalev et J.C. Rajapakse. Dans ICONIP, invited paper, Singapore, novembre 2002.
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{Kruggel, F. and Chalopin, C. and Descombes, X. and Kovalev, V. and Rajapakse, J.C.}, |
| title |
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{Segmentation of Pathological Features in MRI Brain Datasets}, |
| year |
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{2002}, |
| month |
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{novembre}, |
| booktitle |
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{ICONIP, invited paper}, |
| address |
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{Singapore}, |
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{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1201981}, |
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2 - Fusion of Radiometry and Textural Information for SIRC Image Classification.
O. Viveros-Cancino et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, septembre 2002.
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{Viveros-Cancino, O. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
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{Fusion of Radiometry and Textural Information for SIRC Image Classification}, |
| year |
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{2002}, |
| month |
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{septembre}, |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
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{Rochester, USA}, |
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{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1038916}, |
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|
3 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets.
C. Shaffrey et N. Kingsbury et I. H. Jermyn. Dans Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, septembre 2002.
Mots-clés : Segmentation, Hidden Markov Model, Texture, Couleur.
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{Shaffrey, C. and Kingsbury, N. and Jermyn, I. H.}, |
| title |
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{Unsupervised Image Segmentation via Markov Trees and Complex Wavelets}, |
| year |
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{2002}, |
| month |
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{septembre}, |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
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{Rochester, USA}, |
| pdf |
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{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Shaffrey02icip.pdf}, |
| keyword |
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{Segmentation, Hidden Markov Model, Texture, Couleur} |
| } |
Abstract :
The goal in image segmentation is to label pixels in an image based
on the properties of each pixel and its surrounding region. Recently
Content-Based Image Retrieval (CBIR) has emerged as an
application area in which retrieval is attempted by trying to gain
unsupervised access to the image semantics directly rather than
via manual annotation. To this end, we present an unsupervised
segmentation technique in which colour and texture models are
learned from the image prior to segmentation, and whose output
(including the models) may subsequently be used as a content
descriptor in a CBIR system. These models are obtained in a
multiresolution setting in which Hidden Markov Trees (HMT) are
used to model the key statistical properties exhibited by complex
wavelet and scaling function coefficients. The unsupervised Mean
Shift Iteration (MSI) procedure is used to determine a number of
image regions which are then used to train the models for each
segmentation class. |
|
4 - Psychovisual Evaluation of Image Segmentation Algorithms.
C. Shaffrey et I. H. Jermyn et N. Kingsbury. Dans Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, septembre 2002.
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{Proc. Advanced Concepts for Intelligent Vision Systems}, |
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5 - Evaluation Methodologies for Image Retrieval Systems.
I. H. Jermyn et C. Shaffrey et N. Kingsbury. Dans Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, septembre 2002.
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{Evaluation Methodologies for Image Retrieval Systems}, |
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{Proc. Advanced Concepts for Intelligent Vision Systems}, |
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{http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002final.pdf}, |
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6 - Satellite and aerial image deconvolution using an EM method with complex wavelets.
A. Jalobeanu et R. Nowak et J. Zerubia et M. Figueiredo. Dans Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, septembre 2002.
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{Satellite and aerial image deconvolution using an EM method with complex wavelets}, |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
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7 - Image processing for high resolution satellite and aerial data.
J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Toulouse, France, septembre 2002.
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{Image processing for high resolution satellite and aerial data}, |
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{Proc. European Signal Processing Conference (EUSIPCO)}, |
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8 - Unsupervised segmentation of textured satellite and aerial images with Bayesian methods.
S. Wilson et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Toulouse, France, septembre 2002.
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{Unsupervised segmentation of textured satellite and aerial images with Bayesian methods}, |
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9 - A Gauss-Markov Model for Hyperspectral Texture Analysis of Urban Areas.
G. Rellier et X. Descombes et J. Zerubia et F. Falzon. Dans Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, août 2002.
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{Rellier, G. and Descombes, X. and Zerubia, J. and Falzon, F.}, |
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{A Gauss-Markov Model for Hyperspectral Texture Analysis of Urban Areas}, |
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10 - Object Point Processes for Image Segmentation.
S. Drot et X. Descombes et H. Le Men et J. Zerubia. Dans Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, août 2002.
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11 - A variational approach to one dimensional phase unwrapping.
C. Lacombe et P. Kornprobst et G. Aubert et L. Blanc-Féraud. Dans Proc. International Conference on Pattern Recognition (ICPR), Québec, Canada, août 2002.
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{A variational approach to one dimensional phase unwrapping}, |
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{Proc. International Conference on Pattern Recognition (ICPR)}, |
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12 - Estimation of blur and noise parameters in remote sensing.
A. Jalobeanu et L. Blanc-Féraud et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Orlando, USA, mai 2002.
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{Estimation of blur and noise parameters in remote sensing}, |
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{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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|
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9 Rapports de recherche et Rapports techniques |
1 - Supervised Classification for Textured Images.
J.F. Aujol et G. Aubert et L. Blanc-Féraud. Rapport de Recherche 4640, Inria, France, novembre 2002.
Mots-clés : Texture, Classification, Ondelettes, Equation aux derivees partielles, Courbes de niveaux.
@TECHREPORT{4640,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
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{Supervised Classification for Textured Images}, |
| year |
= |
{2002}, |
| month |
= |
{novembre}, |
| institution |
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{Inria}, |
| type |
= |
{Research Report}, |
| number |
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{4640}, |
| address |
= |
{France}, |
| url |
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{https://hal.inria.fr/inria-00071945}, |
| pdf |
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{https://hal.inria.fr/file/index/docid/71945/filename/RR-4640.pdf}, |
| ps |
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{https://hal.inria.fr/docs/00/07/19/45/PS/RR-4640.ps}, |
| keyword |
= |
{Texture, Classification, Ondelettes, Equation aux derivees partielles, Courbes de niveaux} |
| } |
Résumé :
Dans ce rapport, nous présentons un modèle de classification supervisée basé sur une approche variationnelle. Ce modèle s'applique spécifiquement aux images texturées. Nous souhaitons obtenir une partition optimale de l'image constituée de textures séparées par des interfaces régulières. Pour cela, nous représentons les régions définies par les classes ainsi que leurs interfaces par des fonctions d'ensemble de niveaux. Nous définissons une fonctionnelle sur ces ensembles de niveaux dont le minimum est une partition optimale. Cette fonctionnelle comporte en particulier un terme d'attache aux données spécifique aux textures. Nous utilisons une transformée en paquets d'ondelettes pour analyser les textures, ces dernières étant caractérisées par la distribution de leur énergie dans chaque sous-bande de la décompositon. Les équations aux dérivées partielles (EDP) relatives à la minimisation de la fonctionnelle sont couplées et plongées dans un schéma dynamique. En fixant un ensemble de niveaux initial, les différents termes des EDP guident l'évolution des interfaces (ensemble de niveau zéro) vers les frontières de la partion optimale, par le biais de forces externes (régularité de l'interface) et internes (attache aux données et contraintes partition). Nous avons effectué des tests sur des images synthétiques et sur des images réelles. |
Abstract :
In this report, we present a supervised classification model based on a variational approach. This model is specifically devoted to textured images. We want to get an optimal partition of an image which is composed of textures separated by regular interfaces. To reach this goal, we represent the regions defined by the classes as well as their interfaces by level set functions. We define a functional on these level sets whose minimizers define an optimal partition. In particular, this functional owns a data term specific to textures. We use a packet wavelet transform to analyze the textures, these ones being characterized by their energy distribution in each sub-band of the decomposition. The partial differential equations (PDE) related to the minimization of the functional are embeded in a dynamical scheme. Given an initial interface set (zero level set), the different terms of the PDE's govern the motion of interfaces such that, at convergence, we get an optimal partition as defined above. Each interface is guided by external forces (regularity of the interface), and internal ones (data term and partition constraints). We have conducted several experiments on both synthetic and real images. |
|
2 - On Bayesian Estimation in Manifolds.
I. H. Jermyn. Rapport de Recherche 4607, Inria, France, novembre 2002.
Mots-clés : Évenement rare, Estimation bayesienne, Invariant.
@TECHREPORT{4607,
|
| author |
= |
{Jermyn, I. H.}, |
| title |
= |
{On Bayesian Estimation in Manifolds}, |
| year |
= |
{2002}, |
| month |
= |
{novembre}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
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{4607}, |
| address |
= |
{France}, |
| url |
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{https://hal.inria.fr/inria-00071978}, |
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| } |
Résumé :
Il est fréquemment dit que les estimées au sens du maximum a posteriori (MAP) et du minimum de l'erreur quadratique moyenne (MMSE) d'un paramètre continu ne sont pas invariantes relativement aux «reparamètrisations» de l'espace des paramètres . Ce rapport clarifie les questions autour de ce problème, en soulignant la différence entre l'invariance aux changements de coordonnées, qui est une condition sine qua non pour un problème mathématiq- uement bien défini, et l'invariance aux difféomorphismes, qui est une question significative, et fournit une solution. On montre d'abord que la présence d'une structure métrique sur peut être utilisée pour définir les estimées aux sens du MAP et du MMSE qui sont invariantes aux changements de coordonnées, et on explique pourquoi cela est la fa on naturelle et nécessaire pour le faire. Le problème de l'estimation et les quantités géométriques qui y sont associées sont tous définis d'une fa on clairement invariante aux changements de coordonnées. On montre que la même estimée au sens du MAP est obtenue en utilisant soit la `maximisation d'une densité' soit une fonction de perte delta, définie de fa on invariante. Puis, on discute le choix d'une métrique pour . En imposant un critère d'invariance qui est naturel dans le cadre bayesien, on montre que ce choix est unique. Il ne correspond pas nécessairement à un choix de coordonnées. L'estimée au sens du MAP qui en résulte coincide avec l'estimée fondée sur la longueur minimum de message (MML), mais la demonstration n'utilise pas de discrétisation ou d'approximation. |
Abstract :
It is frequently stated that the maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter are not invariant to arbitrary «reparametrizations» of the parameter space . This report clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and provides a solution. We first show that the presence of a metric structure on can be used to define coordinate-invari- ant MAP and MMSE estimates, and we argue that this is the natural and necessary way to proceed. The estimation problem and related geometrical quantities are all defined in a manifestly coordinate-invariant way. We show that the same MAP estimate results from `density maximization' or from using an invariantly-defined delta function loss. We then discuss the choice of a metric structure on . By imposing an invariance criterion natural within a Bayesian framework, we show that this choice is essentially unique. It does not necessarily correspond to a choice of coordinates. The resulting MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation. |
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Rapport d'activité 2011
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