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|
The Publications
Result of the query in the list of publications :
90 Technical and Research Reports |
84 - Indexing and retrieval in multimedia libraries through parametric texture modeling using the 2D Wold decomposition.
R. Stoica and J. Zerubia and J.M. Francos. Research Report 3594, Inria, December 1998.
Keywords : Markov Fields, Texture, Segmentation, Indexation.
@TECHREPORT{stoica98,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{Indexing and retrieval in multimedia libraries through parametric texture modeling using the 2D Wold decomposition}, |
| year |
= |
{1998}, |
| month |
= |
{December}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3594}, |
| url |
= |
{https://hal.inria.fr/inria-00073085}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73085/filename/RR-3594.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/30/85/PS/RR-3594.ps}, |
| keyword |
= |
{Markov Fields, Texture, Segmentation, Indexation} |
| } |
Résumé :
Ce rapport présente une méthode paramétrique permettant de faire de l'indexati- on et de la recherche dans une base de données multimédia. L'indexation (étiquetage) et la recherche de données multimédia sont réalisées grâce à la modélisation paramétrique de textures qui se trouvent dans les images de la base de données. Les textures sont caracterisées par des paramètres qui servent d'indices pour la recherche dans la base de données. Afin de pouvoir identifier les différentes régions texturées d'une image et estimer les paramètres correspondants, un algorithme de segmentation-estimatio- n est proposé dans ce rapport, qui fait appel à une décomposition de Wold 2D pour le modèle de texture et à un modèle markovien pour l'étiquetage. L'indexation nécessite de définir une distance entre les images. Une nouvelle distance, inspirée de la distance de Kullback, est décrite dans ce rapport. Elle utilise les paramètres estimés correspondants au modèle 2D de chaque texture. Les résultats obtenus relativement à la segmentation et à l'indexatio- n sont proches de ceux obtenus par un opérateur humain. |
Abstract :
This paper presents a parametric method for indexing and retrieval of multimedia data in digital libraries. %Indexing (labeling) and retrieval %of multimedia data, based on the properties %of the imagery components of the stored data record, are derived. Indexing (labeling) and retrieval of the multimedia data are performed using parametric modeling of the textured segments found in the data imagery components. The estimated parametric models of the textured segments serve as their indices, and hence as indices of the entire image, as well as of the multimedia record which the image is part thereof. To achieve the ability to identify textured image regions and estimate their parameters, a joint segmentation-estimation algorithm that combines the 2-D Wold decomposition based texture model with a Markovian labeling process, is derived. Ordering and indexing of images require a definition of a distance measure between images. Using the framework of the Kullback distance between probability distributions, a new rigorous distance measure between textures is derived. The distance between any two textured image segments is evaluated using their estimated parametric models. The proposed segmentation, distance evaluation, and indexing methods are shown to produce comparable results to those obtained by a human viewer. |
|
85 - Image Classification Using a Variational Approach.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. Research Report 3523, Inria, October 1998.
Keywords : Classification, Variational methods.
@TECHREPORT{samsonRR98,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Image Classification Using a Variational Approach}, |
| year |
= |
{1998}, |
| month |
= |
{October}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3523}, |
| url |
= |
{https://hal.inria.fr/inria-00073161}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73161/filename/RR-3523.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/31/61/PS/RR-3523.ps}, |
| keyword |
= |
{Classification, Variational methods} |
| } |
Résumé :
Dans ce rapport nous présentons un modèle variationnel destiné à la classification d'images avec processus de régularisation préservant les contours. La notion de classification étant par nature discrète (i.e. attribuer un label à chaque pixel de l'image), il existe de nombreux modèles de classification par approche probabiliste, mais les modèles variationnels abordant ce sujet sont rares. Ces dernières années, l'approche variationnelle a montré sont efficacité dans le cadre de la restauration d'images avec prise en compte des discontinuités. Dans ce travail, nous ajoutons un processus de classification permettant d'obtenir une solution formée de régions homogènes dont les frontières sont régulières (une région étant définie par l'ensemble des pixels appartenant à la même classe). La justification théorique de notre modèle repose sur les travaux effectués dans le cadre des problèmes de transitions de phases en mécanique. L'algorithme que nous proposons est relativement rapide et facile à mettre en oeuvre. Nous comparons les résultats obtenus sur des images synthétiques et satellitaires avec ceux produits par un modèle stochastique avec régularisation de Potts. |
Abstract :
Herein, we present a variational model devoted to image classification coupled with an edge-preserving regularization process. The discrete nature of classification (i.e. to attribute a label to each pixel) has ledto the development of many probabilistic image classification models, but rarely to variational ones. In the last decade, the variational approach has proven its efficiency in the field of edge-preserving restoration. In this paper we add a classification capability which contributes to provide images compound of homogeneous regions with regularized boundaries, a region being defined as a set of pixels belonging to the same class. The soundness of our model is based on the works developed on the phase transitions theory in mechanics. The proposed algorithm is fast, easy to implement, and efficient. We compare our results on both synthetic and satellite images with the ones obtained by a stochastic model using a Potts regularization. |
|
86 - Mise en correspondance et recalage de graphes : application aux réseaux routiers extraits d'un couple carte/image.
C. Hivernat and X. Descombes and S. Randriamasy and J. Zerubia. Research Report 3529, Inria, October 1998.
Keywords : Markov Fields, Road network, Graph matching.
@TECHREPORT{hiv98,
|
| author |
= |
{Hivernat, C. and Descombes, X. and Randriamasy, S. and Zerubia, J.}, |
| title |
= |
{Mise en correspondance et recalage de graphes : application aux réseaux routiers extraits d'un couple carte/image}, |
| year |
= |
{1998}, |
| month |
= |
{October}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3529}, |
| url |
= |
{https://hal.inria.fr/inria-00073156}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73156/filename/RR-3529.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/31/56/PS/RR-3529.ps}, |
| keyword |
= |
{Markov Fields, Road network, Graph matching} |
| } |
Résumé :
Nous considérons le problème de la mise en correspondance du réseau routier extrait d'une image SPOT avec celui fourni par une base de données cartographi- que. Cette mise en correspondance comprend deux étapes principales fondées sur des modélisations markoviennes. Dans la première étape, les pixels de l'image sont appariés aux segments cartographiques. Le résultat de cette étape permet de découper le réseau obtenu sur l'image sous forme de chaînes. Ces chaînes sont ensuite mises en correspondance avec les segments cartographiques. Pour finir, une étape de qualification des résultats permet de fournir les primitives fiables afin d'affiner le recalage initial. En bouclant l'algorithme sur la mise en correspondance nous obtenons un processus itératif permettant d'améliorer à la fois le recalage et la mise en correspondance. La qualification automatique des résultats est également une aide à l'interprétation pour la mise à jour cartographique. |
Abstract :
We consider herein the matching problem between the road network extracted from a SPOT image and the roads contained in a cartographic database. This matching consists of two main steps based on a Markovian modelling. During the first step, the image road pixels are associated to the map segments. the derived result allows us to split the image network into chains. These chains are matched with the map segments. Finally, an automatic validation procedure provides matched chains/segments which are used to improve the initial registration. An iterative scheme is obtained by performin- g a new matching. The automatic result validation is also helpful for map updating. |
|
87 - Estimation d'hyperparamètres pour la restauration d'images satellitaires par une méthode MCMCML.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. Research Report 3469, Inria, August 1998.
Keywords : Markov Fields, Regularization, Variational methods, Likelihood maximum.
@TECHREPORT{jaloRR98,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Estimation d'hyperparamètres pour la restauration d'images satellitaires par une méthode MCMCML}, |
| year |
= |
{1998}, |
| month |
= |
{August}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3469}, |
| url |
= |
{https://hal.inria.fr/inria-00073221}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73221/filename/RR-3469.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/32/21/PS/RR-3469.ps}, |
| keyword |
= |
{Markov Fields, Regularization, Variational methods, Likelihood maximum} |
| } |
Résumé :
Le problème que nous abordons ici est la déconvolution d'images satellitaires, qui sont dégradées par l'optique et l'électronique utilisées pour leur acquisition. Les dégradations sont connues : les images sont convoluées par un opérateur H, et la variance du bruit N additif, blanc et gaussien, est connue. Nous utilisons un modèle de régularisation introduisant une fonction de potentiel phi, qui interdit l'amplification du bruit lors de la restauration tout en préservant les discontinuités. Ce modèle admet deux hyperparamètres lambda et delta. Nous nous intéressons ici à l'estimation des hyperparamètres optimaux afin d'effectuer la déconvolution de manière automatique. Nous proposons pour cela d'utiliser l'estimateur du maximum de vraisemblance appliqué à l'image observée. Cet estimateur constitue le critère que nous allons optimiser. Pour évaluer ses dérivées, nous devons estimer des espérances calculées sur des échantillon- s, tenant compte des données observées et de l'a priori imposé. Cette probabilité faisant intervenir l'opérateur de convolution, il est très difficile d'utiliser un échantillonneur classique. Nous avons développé un algorithme de type Geman-Yang modifié, utilisant une variable auxiliaire, ainsi qu'une transformée en cosinus. Nous présentons à cette occasion un nouvel algorithme de déconvolution, rapide, qui est dérivé de cette méthode d'échantillonnage. Nous proposons un algorithme "MCMCML" permettant d'effectuer simultanément l'estimation des hyperparamètres lambda et delta et la restauration de l'image dégradée. Une étude des échantillonneurs (y compris ceux de Gibbs et Metropolis), portant sur la vitesse de convergence et les difficultés de calcul liées à l'attache aux données, a également été réalisée. |
Abstract :
This report deals with satellite image restoration. These images are corrupted by an optical blur and electronic noise, due to the physics of the sensors. The degradation model is known : blurring is modeled by convolution, with a linear operator H, and the noise is supposed to be additive, white and Gaussian, with a known variance. The recovery problem is ill-posed and therefore must be regularized. We use a regularization model which introduces a phi function, which avoids noise amplification while preserving image discontinuities (ie. edges) of the restored image. This model exhibits two hyperparameters (lambda and delta). Our goal is to estimate the optimal parameters in order to reconstruct images automatically. Herein, we propose to use the Maximum Likelihood estimator, applied to the observed image. To optimize this criterion, we must estimate expectations by sampling (samples are extracted from a Markov chain) to evaluate its derivatives. These samples are images whose probability takes into account the convolution operator. Thus, it is very difficult to obtain them directly by using a standard sampler. We have developped a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We also present a new reconstruc- tion method based on this sampling algorithm. We detail the MCMCML algorithm which ables to simultaneously estimate lambda and delta parameters, and to reconstruct the corrupted image. An experimental study of samplers (including Gibbs and Metropolis algorithms), with respect to the rate of convergence and the difficulties of dependent data sampling, is also presented in this report. |
|
88 - Extraction des zones urbaines fondée sur une analyse de la texture par modélisation markovienne.
A. Lorette and X. Descombes and J. Zerubia. Research Report 3423, Inria, May 1998.
Keywords : Texture, Markov Fields, Urban areas, Entropy.
@TECHREPORT{loretteRR98,
|
| author |
= |
{Lorette, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction des zones urbaines fondée sur une analyse de la texture par modélisation markovienne}, |
| year |
= |
{1998}, |
| month |
= |
{May}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3423}, |
| url |
= |
{http://hal.inria.fr/inria-00073267}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/07/32/67/PDF/RR-3423.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/32/67/PS/RR-3423.ps}, |
| keyword |
= |
{Texture, Markov Fields, Urban areas, Entropy} |
| } |
Résumé :
Pour délimiter un masque urbain précis à partir d'une image satellitaire la seule information du niveau de gris est insuffisante. Laplupart des méthodes font donc appel à une analyse de la texture de l'image. Nous nous sommes placés dans ce cadre. Dans une première étape, nous avons défini un nouveau paramètre de texture à partir d'un modèle markovien gaussien. Nous obtenons ce nouveau paramètre en calculant la variance conditionnelle de l'image dans huit directions. Ainsi, nous éliminons la mauvaise classification d'objets ayant une orientation privilégiée tels que les vignes et les serres par exemple. Dans une seconde étape, nous proposons un algorithme de emphfuzzy Cmeans modifié incluant un terme d'entropie et pour lequel le nombre de classes n'est pas fixé a priori. Cet algorithme nous permet d'obtenir une première classification de l'image. Enfin, nous régularisons l'image ainsi obtenue grâce à une modélisation par champs de Markov. Des résultats obtenus sur des simulations d'images SPOT5 fournies par le CNES sont présentés. |
Abstract :
Urban areas cannot be extracted from satellite images through only grey level information. Hence most methods analyze the texture of the image to discriminate between urban areas and non urban areas. We define a new texture parameter derived from a Markovian Gaussian model. This new parameter takes into account the variance of the image in eight directions- . Consequently it copes with the misclassification of objects with a privileged orientation like vineyards or greenhouses for instance. Afterwards we develop a modified fuzzy Cmeans algorithm including an entropy term. The advantage of such an algorithm is that the number of classes does not need to be known a priori. By applying this modified fuzzy Cmeans algorithm on the parameter image we obtain a first classification. Finally we regularize the segmented image by using a Markov random field modelling. Some results on SPOT5 simulated images are presented. These images are provided by the CNES (French Space Agency). |
|
89 - An elementary proof of the equivalence between 2D and 3D classical snakes and geodesic active contours.
G. Aubert and L. Blanc-Féraud. Research Report 3340, Inria, January 1998.
Keywords : Geodesic active contours, Partial differential equation.
@TECHREPORT{lbf98i,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{An elementary proof of the equivalence between 2D and 3D classical snakes and geodesic active contours}, |
| year |
= |
{1998}, |
| month |
= |
{January}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3340}, |
| url |
= |
{https://hal.inria.fr/inria-00073349}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/73349/filename/RR-3340.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/33/49/PS/RR-3340.ps}, |
| keyword |
= |
{Geodesic active contours, Partial differential equation} |
| } |
Résumé :
Les équations aux dérivées partielles (EDP) définissant l'évolution de courbe plane permettent, avec une implantation par ligne de niveau, un changement de topologie par rapport à la courbe initiale et sont de ce fait un outil puissant pour la segmentation d'objet dans une image. Récemment, Caselles et al. ont montrés l'équivalence entre les modèles de contours actifs classiques de Kass et al. et de contours actifs géodésiques qui définissent une EDP particulière d'évolution de courbe. Cependant la preuve proposée par Caselles n'est valable que pour la segmentation d'objet d'une image 2D avec des courbes 1D et fait appel à des concepts de la théorie hamiltonienne, sans aucun sens physique pour les contours actifs. Ce papier propose une preuve utilisant uniquement des calculs élémentaires d'analyse mathématique, valables aussi pour la segmentation d'image 3D à l'aide de surface. |
Abstract :
Recently, Caselles et al. have shown in the equivalence between a classical snake problem of Kass et al. and a geodesic active contour model. The PDE derived from the geodesic problem gives an evolution equation for active contours which is very powerfull for image segmentation since changes of topology are allowed using the level set implementation. However in Caselles' paper the equivalence with classical snake is only shown for 2D images with 1D curves, by using concepts of Hamiltonian theory which have no meanings for active contours. This paper propose a proof using only elementary calculus of mathematical analysis. This proof is also valid in the 3D case for active surfaces. |
|
90 - Estimation-segmentation du flot optique et contours actifs.
R. Ciampini and L. Blanc-Féraud and M. Barlaud and E. Salerno. Research Report 98-11, Laboratoire I3S, 1998.
@TECHREPORT{lbf98j,
|
| author |
= |
{Ciampini, R. and Blanc-Féraud, L. and Barlaud, M. and Salerno, E.}, |
| title |
= |
{Estimation-segmentation du flot optique et contours actifs}, |
| year |
= |
{1998}, |
| institution |
= |
{Laboratoire I3S}, |
| type |
= |
{Research Report}, |
| number |
= |
{98-11}, |
| pdf |
= |
{Rapports/RR98-11-I3S.pdf}, |
| keyword |
= |
{} |
| } |
|
 top of the page
14 Collection articles or Books chapters |
1 - Probability Density Function Estimation for Classification of High Resolution SAR Images.
V. Krylov and G. Moser and S. Serduc and J. Zerubia. In Signal Processing for Remote Sensing, Second Edition, pages 339-363, Ed. C. Chen., Publ. Taylor & Francis, February 2012.
@INCOLLECTION{Taylor12,
|
| author |
= |
{Krylov, V. and Moser, G. and Serduc, S. and Zerubia, J.}, |
| title |
= |
{Probability Density Function Estimation for Classification of High Resolution SAR Images}, |
| year |
= |
{2012}, |
| month |
= |
{February}, |
| booktitle |
= |
{Signal Processing for Remote Sensing, Second Edition}, |
| pages |
= |
{339-363}, |
| editor |
= |
{C. Chen.}, |
| publisher |
= |
{Taylor & Francis}, |
| url |
= |
{https://www.crcpress.com/Signal-and-Image-Processing-for-Remote-Sensing-Second-Edition/Chen/9781439855966}, |
| pdf |
= |
{https://hal.inria.fr/hal-00729044/document}, |
| keyword |
= |
{} |
| } |
|
2 - Detection and Recognition of a Collection of Objects in a Scene.
X. Descombes and I. H. Jermyn and J. Zerubia. In Inverse Problems in Vision and 3D Tomography, pages 155--189, series DSIP, Ed. ISTE, London ; John Wiley and Sons, New York, 2010.
@INCOLLECTION{Wiley10,
|
| author |
= |
{Descombes, X. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Detection and Recognition of a Collection of Objects in a Scene}, |
| year |
= |
{2010}, |
| booktitle |
= |
{Inverse Problems in Vision and 3D Tomography}, |
| pages |
= |
{155--189}, |
| series |
= |
{DSIP}, |
| editor |
= |
{ISTE, London ; John Wiley and Sons, New York}, |
| url |
= |
{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1848211724.html}, |
| pdf |
= |
{http://onlinelibrary.wiley.com/doi/10.1002/9781118603864.ch5/summary}, |
| keyword |
= |
{} |
| } |
|
3 - Blind Image Deconvolution.
L. Blanc-Féraud and Mugnier L. and A. Jalobeanu. In Inverse Problems in Vision and 3D Tomography, pages 97-121, Ed. series DSIP, Ed. ISTE, London ; John Wiley and Sons, New York, 2010.
@INCOLLECTION{BlindIP2010,
|
| author |
= |
{Blanc-Féraud, L. and L., Mugnier and Jalobeanu, A.}, |
| title |
= |
{Blind Image Deconvolution}, |
| year |
= |
{2010}, |
| booktitle |
= |
{Inverse Problems in Vision and 3D Tomography}, |
| pages |
= |
{97-121}, |
| editor |
= |
{series DSIP, Ed. ISTE, London ; John Wiley and Sons, New York}, |
| url |
= |
{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1848211724.html}, |
| pdf |
= |
{http://onlinelibrary.wiley.com/doi/10.1002/9781118603864.ch3/summary}, |
| keyword |
= |
{} |
| } |
|
4 - Deconvolution aveugle d'une image.
L. Blanc-Féraud and Mugnier L. and A. Jalobeanu. In Problemes inverses en imagerie et en vision, pages 107-132, series Tr. IC2, Ed. Ali Mohammad-Djafari, Publ. Ed. Hermes, 2009.
Copyright : Ed. Hermes
@INCOLLECTION{BLANC_FERAUD_DECAVEUGLE,
|
| author |
= |
{Blanc-Féraud, L. and L., Mugnier and Jalobeanu, A.}, |
| title |
= |
{Deconvolution aveugle d'une image}, |
| year |
= |
{2009}, |
| booktitle |
= |
{Problemes inverses en imagerie et en vision}, |
| pages |
= |
{107-132}, |
| series |
= |
{Tr. IC2}, |
| editor |
= |
{Ali Mohammad-Djafari}, |
| publisher |
= |
{Ed. Hermes}, |
| url |
= |
{http://www.lavoisier.fr/livre/electricite-electronique/problemes-inverses-en-imagerie-et-en-vision-en-2-volumes-inseparables/mohammad-djafari/descriptif-9782746219977}, |
| keyword |
= |
{} |
| } |
|
5 - Detection d’objets dans une scene.
X. Descombes and I. H. Jermyn and J. Zerubia. In Problemes inverses en imagerie et en vision, pages 167--204, series Tr. IC2, Ed. Ali Mohammad-Djafari, Publ. Ed. Hermes, 2009.
Copyright : Ed. Hermes
@INCOLLECTION{DESCOMBES_DETECTION,
|
| author |
= |
{Descombes, X. and Jermyn, I. H. and Zerubia, J.}, |
| title |
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{Detection d’objets dans une scene}, |
| year |
= |
{2009}, |
| booktitle |
= |
{Problemes inverses en imagerie et en vision}, |
| pages |
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{167--204}, |
| series |
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{Tr. IC2}, |
| editor |
= |
{Ali Mohammad-Djafari}, |
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{Ed. Hermes}, |
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{http://www.lavoisier.fr/livre/electricite-electronique/problemes-inverses-en-imagerie-et-en-vision-en-2-volumes-inseparables/mohammad-djafari/descriptif-9782746219977}, |
| keyword |
= |
{} |
| } |
|
6 - Deconvolution of images.
J. Idier and L. Blanc-Féraud. In Bayesian approach to inverse problems, Ed. J. Idier, Publ. ISTE and John Wiley & Sons, 2008.
Keywords : Inverse Problems, Bayesian approach, Image procressing.
Copyright : ISTE Ltd
@INCOLLECTION{blancferaud08,
|
| author |
= |
{Idier, J. and Blanc-Féraud, L.}, |
| title |
= |
{Deconvolution of images}, |
| year |
= |
{2008}, |
| booktitle |
= |
{Bayesian approach to inverse problems}, |
| editor |
= |
{J. Idier}, |
| publisher |
= |
{ISTE and John Wiley & Sons}, |
| url |
= |
{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1848210329.html}, |
| pdf |
= |
{http://onlinelibrary.wiley.com/doi/10.1002/9780470611197.ch6/summary}, |
| keyword |
= |
{Inverse Problems, Bayesian approach, Image procressing} |
| } |
|
7 - Unsupervised problems.
X. Descombes and Y. Goussard. In Bayesian approach to inverse problems, Ed. J. Idier, Publ. ISTE and John Wiley & Sons, 2008.
Note : to appear.
Keywords : Inverse Problems, Bayesian approach, Image procressing.
Copyright : ISTE Ltd
@INCOLLECTION{descombes08,
|
| author |
= |
{Descombes, X. and Goussard, Y.}, |
| title |
= |
{Unsupervised problems}, |
| year |
= |
{2008}, |
| booktitle |
= |
{Bayesian approach to inverse problems}, |
| editor |
= |
{J. Idier}, |
| publisher |
= |
{ISTE and John Wiley & Sons}, |
| url |
= |
{http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1848210329.html}, |
| pdf |
= |
{http://onlinelibrary.wiley.com/doi/10.1002/9780470611197.ch8/summary}, |
| keyword |
= |
{Inverse Problems, Bayesian approach, Image procressing} |
| } |
|
8 - Bayesian estimation of blur and noise in remote sensing imaging.
A. Jalobeanu and J. Zerubia and L. Blanc-Féraud. In Blind image deconvolution: theory and applications, Ed. P. Campisi and K. Egiazarian, Publ. CRC Press, 2007.
@INCOLLECTION{jalo2006,
|
| author |
= |
{Jalobeanu, A. and Zerubia, J. and Blanc-Féraud, L.}, |
| title |
= |
{Bayesian estimation of blur and noise in remote sensing imaging}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Blind image deconvolution: theory and applications}, |
| editor |
= |
{P. Campisi and K. Egiazarian}, |
| publisher |
= |
{CRC Press}, |
| url |
= |
{https://www.crcpress.com/Blind-Image-Deconvolution-Theory-and-Applications/Campisi-Egiazarian/9780849373671}, |
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= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_jalo2006.pdf}, |
| keyword |
= |
{} |
| } |
|
9 - Wulff Shapes at Zero Temperature for Some Models Used in Image Processing.
X. Descombes and E. Pechersky. In Statistics and Analysis of Shapes, pages 281-302, Ed. H. Krim and A. Yezzi, Publ. Birkhauser, 2006.
@INCOLLECTION{DesPechBook,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Wulff Shapes at Zero Temperature for Some Models Used in Image Processing}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Statistics and Analysis of Shapes}, |
| pages |
= |
{281-302}, |
| editor |
= |
{H. Krim and A. Yezzi}, |
| publisher |
= |
{Birkhauser}, |
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= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_DesPechBook.pdf}, |
| keyword |
= |
{} |
| } |
|
10 - A Reversible Jump MCMC Sampler for Object Detection in Image Processing.
M. Ortner and X. Descombes and J. Zerubia. In Monte Carlo and Quasi-Monte Carlo Methods, Publ. Springer Verlag, 2005.
Keywords : RJMCMC, Marked point process, Object extraction.
@INCOLLECTION{ortner_lnsmc2qmc,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Reversible Jump MCMC Sampler for Object Detection in Image Processing}, |
| year |
= |
{2005}, |
| booktitle |
= |
{Monte Carlo and Quasi-Monte Carlo Methods}, |
| publisher |
= |
{Springer Verlag}, |
| url |
= |
{http://link.springer.com/book/10.1007/3-540-31186-6}, |
| pdf |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-31186-6_23}, |
| keyword |
= |
{RJMCMC, Marked point process, Object extraction} |
| } |
|
11 - Markov random fields in image processing, application to remote sensing and astrophysics.
J. Zerubia and A. Jalobeanu and Z. Kato. In Journal de Physique, EDP Sciences, Vol. IV (12), 2002.
Keywords : Markov Fields, Satellite images, Astrophysics.
@INCOLLECTION{jzjalokato2002,
|
| author |
= |
{Zerubia, J. and Jalobeanu, A. and Kato, Z.}, |
| title |
= |
{Markov random fields in image processing, application to remote sensing and astrophysics}, |
| year |
= |
{2002}, |
| booktitle |
= |
{Journal de Physique, EDP Sciences}, |
| volume |
= |
{IV}, |
| number |
= |
{12}, |
| url |
= |
{http://jp4.journaldephysique.org/articles/jp4/abs/2002/01/jp4pr1p117/jp4pr1p117.html}, |
| keyword |
= |
{Markov Fields, Satellite images, Astrophysics} |
| } |
|
12 - An application of marked point process to the extraction of linear networks for images.
R. Stoica and X. Descombes and M.N.M. Van Lieshout and J. Zerubia. In Spatial statitics through applications, Publ. WITPress, 2002.
Keywords : Line networks, Road network, Object extraction, Satellite images, Marked point process.
@INCOLLECTION{stoicaXDlivre,
|
| author |
= |
{Stoica, R. and Descombes, X. and Van Lieshout, M.N.M. and Zerubia, J.}, |
| title |
= |
{An application of marked point process to the extraction of linear networks for images}, |
| year |
= |
{2002}, |
| booktitle |
= |
{Spatial statitics through applications}, |
| publisher |
= |
{WITPress}, |
| url |
= |
{http://www.witpress.com/books/978-1-85312-649-9}, |
| pdf |
= |
{http://oai.cwi.nl/oai/asset/10645/10645A.pdf}, |
| keyword |
= |
{Line networks, Road network, Object extraction, Satellite images, Marked point process} |
| } |
|
13 - Problèmes non supervisés.
X. Descombes and Y. Goussard. In Approche bayésienne pour les problèmes inverses, Ed. J. Idier, Publ. Hermes, 2001.
Copyright :
@INCOLLECTION{GoussardXD01,
|
| author |
= |
{Descombes, X. and Goussard, Y.}, |
| title |
= |
{Problèmes non supervisés}, |
| year |
= |
{2001}, |
| booktitle |
= |
{Approche bayésienne pour les problèmes inverses}, |
| editor |
= |
{J. Idier}, |
| publisher |
= |
{Hermes}, |
| url |
= |
{http://editions.lavoisier.fr/mathematiques/approche-bayesienne-pour-les-problemes-inverses/idier/hermes-science-publications/traite-ic2/livre/9782746203488}, |
| keyword |
= |
{} |
| } |
|
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