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Publications of 2002
Result of the query in the list of publications :
4 Articles |
1 - Marked Point Processes in Image Analysis.
X. Descombes and J. Zerubia. IEEE Signal Processing Magazine, 19(5): pages 77-84, September 2002.
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2 - Extension of phase correlation to subpixel registration.
H. Foroosh and J. Zerubia and M. Berthod. IEEE Trans. on Image Processing, 11(3): pages 188 - 200, March 2002.
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{Foroosh, H. and Zerubia, J. and Berthod, M.}, |
| title |
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{Extension of phase correlation to subpixel registration}, |
| year |
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{2002}, |
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{March}, |
| journal |
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{IEEE Trans. on Image Processing}, |
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{11}, |
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{3}, |
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{188 - 200}, |
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{http://ieeexplore.ieee.org/iel5/83/21305/00988953.pdf?tp=&arnumber=988953&isnumber=21305}, |
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{} |
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|
3 - Local registration and deformation of a road cartographic database on a SPOT Satellite Image.
G. Rellier and X. Descombes and J. Zerubia. Pattern Recognition, 35(10), 2002.
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|
| author |
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{Rellier, G. and Descombes, X. and Zerubia, J.}, |
| title |
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{Local registration and deformation of a road cartographic database on a SPOT Satellite Image}, |
| year |
= |
{2002}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
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{35}, |
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{10}, |
| url |
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{http://www.sciencedirect.com/science/article/pii/S0031320301001807}, |
| keyword |
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{} |
| } |
|
4 - Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. Pattern Recognition, 35(2): pages 341--352, 2002.
@ARTICLE{jalo02h,
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| author |
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{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
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{Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method}, |
| year |
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{2002}, |
| journal |
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{Pattern Recognition}, |
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{35}, |
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{341--352}, |
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{http://www.sciencedirect.com/science/article/pii/S0031320300001783}, |
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{} |
| } |
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2 PhD Thesis and Habilitations |
1 - Segmentation d'images d'observation de la Terre par des techniques de géométrie probabiliste.
S. Drot. PhD Thesis, Universite de Nice Sophia Antipolis, December 2002.
Note : papier (tu-0758)
@PHDTHESIS{drot,
|
| author |
= |
{Drot, S.}, |
| title |
= |
{Segmentation d'images d'observation de la Terre par des techniques de géométrie probabiliste}, |
| year |
= |
{2002}, |
| month |
= |
{December}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| note |
= |
{papier (tu-0758)}, |
| keyword |
= |
{} |
| } |
|
2 - Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes.
G. Rellier. PhD Thesis, Universite de Nice Sophia Antipolis, November 2002.
Keywords : Hyperspectral imaging, Texture, Classification, Markov Fields.
@PHDTHESIS{rellier,
|
| author |
= |
{Rellier, G.}, |
| title |
= |
{Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes}, |
| year |
= |
{2002}, |
| month |
= |
{November}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00505898}, |
| keyword |
= |
{Hyperspectral imaging, Texture, Classification, Markov Fields} |
| } |
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 Conference articles |
1 - Segmentation of Pathological Features in MRI Brain Datasets.
F. Kruggel and C. Chalopin and X. Descombes and V. Kovalev and J.C. Rajapakse. In ICONIP, invited paper, Singapore, November 2002.
@INPROCEEDINGS{kruggelXd,
|
| author |
= |
{Kruggel, F. and Chalopin, C. and Descombes, X. and Kovalev, V. and Rajapakse, J.C.}, |
| title |
= |
{Segmentation of Pathological Features in MRI Brain Datasets}, |
| year |
= |
{2002}, |
| month |
= |
{November}, |
| booktitle |
= |
{ICONIP, invited paper}, |
| address |
= |
{Singapore}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1201981}, |
| keyword |
= |
{} |
| } |
|
2 - Fusion of Radiometry and Textural Information for SIRC Image Classification.
O. Viveros-Cancino and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002.
@INPROCEEDINGS{oscarbaghdadi,
|
| author |
= |
{Viveros-Cancino, O. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Fusion of Radiometry and Textural Information for SIRC Image Classification}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Rochester, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1038916}, |
| keyword |
= |
{} |
| } |
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3 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets.
C. Shaffrey and N. Kingsbury and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002.
Keywords : Segmentation, Hidden Markov Model, Texture, Colour.
@INPROCEEDINGS{ijking,
|
| author |
= |
{Shaffrey, C. and Kingsbury, N. and Jermyn, I. H.}, |
| title |
= |
{Unsupervised Image Segmentation via Markov Trees and Complex Wavelets}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Rochester, USA}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Shaffrey02icip.pdf}, |
| keyword |
= |
{Segmentation, Hidden Markov Model, Texture, Colour} |
| } |
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. |
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4 - Psychovisual Evaluation of Image Segmentation Algorithms.
C. Shaffrey and I. H. Jermyn and N. Kingsbury. In Proc. Advanced Concepts for Intelligent Vision Systems, Ghent, Belgique, September 2002.
@INPROCEEDINGS{kingij,
|
| author |
= |
{Shaffrey, C. and Jermyn, I. H. and Kingsbury, N.}, |
| title |
= |
{Psychovisual Evaluation of Image Segmentation Algorithms}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Ghent, Belgique}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/acivs2002_final.pdf}, |
| keyword |
= |
{} |
| } |
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