Publications de 2002

Résultat de la recherche dans la liste des publications :

• 4 Articles
• 2 Thèses de Doctorat et Habilitations
• 12 Articles de conférence
• 9 Rapports de recherche et Rapports techniques
• 2 Articles de collection ou Chapitres de livres

nouvelle recherche | bibtex

navigation : 1 - 2 - 3 affichage : 10 par page | 20 par page | 50 par page

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. @ARTICLE{XDJZ, author = {Descombes, X. and Zerubia, J.}, title = {Marked Point Processes in Image Analysis}, year = {2002}, month = {septembre}, journal = {IEEE Signal Processing Magazine}, volume = {19}, number = {5}, pages = {77-84}, pdf = {http://ieeexplore.ieee.org/iel5/79/22084/01028354.pdf?tp=&arnumber=1028354&isnumber=22084}, keyword = {} }

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. @ARTICLE{forooshjzmb, author = {Foroosh, H. and Zerubia, J. and Berthod, M.}, title = {Extension of phase correlation to subpixel registration}, year = {2002}, month = {mars}, journal = {IEEE Trans. on Image Processing}, volume = {11}, number = {3}, pages = {188 - 200}, pdf = {http://ieeexplore.ieee.org/iel5/83/21305/00988953.pdf?tp=&arnumber=988953&isnumber=21305}, keyword = {} }

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. @ARTICLE{rellierXDJZ, author = {Rellier, G. and Descombes, X. and Zerubia, J.}, title = {Local registration and deformation of a road cartographic database on a SPOT Satellite Image}, year = {2002}, journal = {Pattern Recognition}, volume = {35}, number = {10}, url = {http://www.sciencedirect.com/science/article/pii/S0031320301001807}, keyword = {} }

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. @ARTICLE{jalo02h, author = {Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, title = {Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method}, year = {2002}, journal = {Pattern Recognition}, volume = {35}, number = {2}, pages = {341--352}, url = {http://www.sciencedirect.com/science/article/pii/S0031320300001783}, keyword = {} }

haut de la page

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) @PHDTHESIS{drot, author = {Drot, S.}, title = {Segmentation d'images d'observation de la Terre par des techniques de géométrie probabiliste}, year = {2002}, month = {décembre}, 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. Thèse de Doctorat, Universite de Nice Sophia Antipolis, novembre 2002. Mots-clés : Imagerie hyperspectrale, Texture, Classification, Champs de Markov. @PHDTHESIS{rellier, author = {Rellier, G.}, title = {Analyse de texture dans l'espace hyperspectral par des méthodes probabilistes}, year = {2002}, month = {novembre}, school = {Universite de Nice Sophia Antipolis}, url = {https://hal.inria.fr/tel-00505898}, keyword = {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.

haut de la page

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. @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 = {novembre}, 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 et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, septembre 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 = {septembre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Rochester, USA}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1038916}, keyword = {} }

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. @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 = {septembre}, 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, 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. @INPROCEEDINGS{kingij, author = {Shaffrey, C. and Jermyn, I. H. and Kingsbury, N.}, title = {Psychovisual Evaluation of Image Segmentation Algorithms}, year = {2002}, month = {septembre}, 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 = {} }

haut de la page

navigation : 1 - 2 - 3 affichage : 10 par page | 20 par page | 50 par page

Ces pages sont générées par