|
|
Publications of Josiane Zerubia
Result of the query in the list of publications :
173 Conference articles |
142 - Apport de l'imagerie radar pour l'extraction des zones urbaines.
O. Viveros-Cancino and X. Descombes and J. Zerubia. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Cahors, France, June 2001.
@INPROCEEDINGS{xd01j,
|
| author |
= |
{Viveros-Cancino, O. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Apport de l'imagerie radar pour l'extraction des zones urbaines}, |
| year |
= |
{2001}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Cahors, France}, |
| url |
= |
{http://www.irit.fr/ORASIS2001/}, |
| ps |
= |
{http://www.irit.fr/ORASIS2001/images/docs/viveros.ps.gz}, |
| keyword |
= |
{} |
| } |
|
143 - Estimation rapide du paramètre de régularisation en déconvolution d'images.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Cahors, France, June 2001.
@INPROCEEDINGS{aj01c,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Estimation rapide du paramètre de régularisation en déconvolution d'images}, |
| year |
= |
{2001}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Cahors, France}, |
| url |
= |
{http://www.irit.fr/ORASIS2001/}, |
| ps |
= |
{http://www.irit.fr/ORASIS2001/images/docs/jalobeanu.ps.gz}, |
| keyword |
= |
{} |
| } |
|
144 - Modelling images with alpha-stable textures.
E.E. Kuruoglu and J. Zerubia. In Physics in Signal and Image Processing, Marseille, France, January 2001.
@INPROCEEDINGS{KuruJZ01,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling images with alpha-stable textures}, |
| year |
= |
{2001}, |
| month |
= |
{January}, |
| booktitle |
= |
{Physics in Signal and Image Processing}, |
| address |
= |
{Marseille, France}, |
| keyword |
= |
{} |
| } |
|
145 - Segmentation d'image haute résolution par processus Markov objet.
X. Descombes and S. Drot and M. Imberty and H. Le Men and J. Zerubia. In Séminaire Télédétection à très haute résolution spatiale et analyse d'image, Cemagref, Montpellier, France, 2001.
@INPROCEEDINGS{xd01k,
|
| author |
= |
{Descombes, X. and Drot, S. and Imberty, M. and Le Men, H. and Zerubia, J.}, |
| title |
= |
{Segmentation d'image haute résolution par processus Markov objet}, |
| year |
= |
{2001}, |
| booktitle |
= |
{Séminaire Télédétection à très haute résolution spatiale et analyse d'image, Cemagref}, |
| address |
= |
{Montpellier, France}, |
| url |
= |
{http://cemadoc.irstea.fr/oa/PUB00009549-segmentation-image-haute-resolution-par-processus.html}, |
| keyword |
= |
{} |
| } |
|
146 - Regularisation by convolution in probability density estimation is equivalent to jittering.
C. Molina and J. Zerubia. In Proc. IEEE International Workshop on Neural Networks for Signal Processing (NNSP), Sydney, Australie, December 2000.
@INPROCEEDINGS{jz,
|
| author |
= |
{Molina, C. and Zerubia, J.}, |
| title |
= |
{Regularisation by convolution in probability density estimation is equivalent to jittering}, |
| year |
= |
{2000}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. IEEE International Workshop on Neural Networks for Signal Processing (NNSP)}, |
| address |
= |
{Sydney, Australie}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=889411}, |
| keyword |
= |
{} |
| } |
|
147 - Modelling SAR images with a generalisation of the Raylegh distribution.
E.E. Kuruoglu and J. Zerubia. In Asilomar Conference, Pacific Grove, USA, October 2000.
@INPROCEEDINGS{jz00w,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling SAR images with a generalisation of the Raylegh distribution}, |
| year |
= |
{2000}, |
| month |
= |
{October}, |
| booktitle |
= |
{Asilomar Conference}, |
| address |
= |
{Pacific Grove, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=910949}, |
| keyword |
= |
{} |
| } |
|
148 - Satellite image deconvolution using complex wavelet packets.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vancouver, Canada, September 2000.
@INPROCEEDINGS{jalo00c,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Satellite image deconvolution using complex wavelet packets}, |
| year |
= |
{2000}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Vancouver, Canada}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=899579}, |
| keyword |
= |
{} |
| } |
|
149 - Estimation of adaptive parameters for satellite image deconvolution.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Barcelone, Espagne, September 2000.
@INPROCEEDINGS{jalo00d,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Estimation of adaptive parameters for satellite image deconvolution}, |
| year |
= |
{2000}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Barcelone, Espagne}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=903549}, |
| keyword |
= |
{} |
| } |
|
150 - Deformation of a Cartographic Road Network on a SPOT Satellite Image.
G. Rellier and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vancouver, Canada, September 2000.
@INPROCEEDINGS{rel00a,
|
| author |
= |
{Rellier, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Deformation of a Cartographic Road Network on a SPOT Satellite Image}, |
| year |
= |
{2000}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Vancouver, Canada}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=899814}, |
| keyword |
= |
{} |
| } |
|
151 - Fully unsupervised fuzzy clustering with entropy criterion.
A. Lorette and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Barcelone, Espagne, September 2000.
@INPROCEEDINGS{xd00c,
|
| author |
= |
{Lorette, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Fully unsupervised fuzzy clustering with entropy criterion}, |
| year |
= |
{2000}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Barcelone, Espagne}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=903710}, |
| keyword |
= |
{} |
| } |
|
152 - Road extraction in remote sensed images using a stochastic geometry framework.
R. Stoica and X. Descombes and J. Zerubia. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Gif sur Yvette, France, July 2000.
@INPROCEEDINGS{xd00b,
|
| author |
= |
{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Road extraction in remote sensed images using a stochastic geometry framework}, |
| year |
= |
{2000}, |
| month |
= |
{July}, |
| booktitle |
= |
{Bayesian Inference and Maximum Entropy Methods in Science and Engineering}, |
| address |
= |
{Gif sur Yvette, France}, |
| url |
= |
{http://www-prod-gif.supelec.fr/invi/lss/MaxEnt2000/}, |
| pdf |
= |
{http://www-prod-gif.supelec.fr/invi/lss/MaxEnt2000/htm/Abstracts/stoica.pdf}, |
| keyword |
= |
{} |
| } |
|
153 - Multiphase evolution and variational image classification.
L. Blanc-Féraud and C. Samson and G. Aubert and J. Zerubia. In Congress SIMAI, Ischia, Italie, June 2000.
Note : papier
@INPROCEEDINGS{lbf00a,
|
| author |
= |
{Blanc-Féraud, L. and Samson, C. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Multiphase evolution and variational image classification}, |
| year |
= |
{2000}, |
| month |
= |
{June}, |
| booktitle |
= |
{Congress SIMAI}, |
| address |
= |
{Ischia, Italie}, |
| note |
= |
{papier}, |
| url |
= |
{http://opac.inria.fr/record=b1115247}, |
| keyword |
= |
{} |
| } |
|
154 - Une approche variationnelle pour la classification d'images par régions actives.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Reconnaissance des formes et Intelligence Artificielle, Paris, France, February 2000.
@INPROCEEDINGS{cs00a,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Une approche variationnelle pour la classification d'images par régions actives}, |
| year |
= |
{2000}, |
| month |
= |
{February}, |
| booktitle |
= |
{Proc. Reconnaissance des formes et Intelligence Artificielle}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{Articles/rfia2000.pdf}, |
| keyword |
= |
{} |
| } |
|
155 - Modélisation markovienne multi-directionnelle: Application à l'extraction des zones urbaines.
A. Lorette and X. Descombes and J. Zerubia. In Proc. Reconnaissance des formes et Intelligence Artificielle, Paris, France, February 2000.
Note : papier
@INPROCEEDINGS{xd00d,
|
| author |
= |
{Lorette, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Modélisation markovienne multi-directionnelle: Application à l'extraction des zones urbaines}, |
| year |
= |
{2000}, |
| month |
= |
{February}, |
| booktitle |
= |
{Proc. Reconnaissance des formes et Intelligence Artificielle}, |
| address |
= |
{Paris, France}, |
| note |
= |
{papier}, |
| url |
= |
{http://opac.inria.fr/record=b1114970*frf}, |
| keyword |
= |
{} |
| } |
|
156 - Two Markov point processes for simulating line networks.
X. Descombes and R. Stoica and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Kobe, Japon, October 1999.
@INPROCEEDINGS{xd99g,
|
| author |
= |
{Descombes, X. and Stoica, R. and Zerubia, J.}, |
| title |
= |
{Two Markov point processes for simulating line networks}, |
| year |
= |
{1999}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Kobe, Japon}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=822850}, |
| keyword |
= |
{} |
| } |
|
157 - Texture Analysis through Markov Random Fields: Urban Areas Extractions.
A. Lorette and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Kobe, Japon, October 1999.
@INPROCEEDINGS{xd99j,
|
| author |
= |
{Lorette, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Texture Analysis through Markov Random Fields: Urban Areas Extractions}, |
| year |
= |
{1999}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Kobe, Japon}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=819629}, |
| keyword |
= |
{} |
| } |
|
158 - Restauration automatique d'images satellitaires par une méthode MCMC.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Vannes, France, September 1999.
@INPROCEEDINGS{aj99c,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Restauration automatique d'images satellitaires par une méthode MCMC}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Vannes, France}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/12942}, |
| keyword |
= |
{} |
| } |
|
159 - A Level Set Model for Image Classification.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Scale-Space, Corfu, Grèce, September 1999.
@INPROCEEDINGS{cs99d,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{A Level Set Model for Image Classification}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. Scale-Space}, |
| address |
= |
{Corfu, Grèce}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-48236-9_27}, |
| keyword |
= |
{} |
| } |
|
160 - Graph-matching model using Gibbsian modeling : application to map-SPOT image road networks for map updating.
X. Descombes and C. Hivernat and S. Randriamasy and J. Zerubia. In Proc. SPIE Conference on Wavelets, Denver, USA, July 1999.
@INPROCEEDINGS{xd99i,
|
| author |
= |
{Descombes, X. and Hivernat, C. and Randriamasy, S. and Zerubia, J.}, |
| title |
= |
{Graph-matching model using Gibbsian modeling : application to map-SPOT image road networks for map updating}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. SPIE Conference on Wavelets}, |
| address |
= |
{Denver, USA}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=906165}, |
| keyword |
= |
{} |
| } |
|
161 - Hyperparameter estimation for satellite image restoration by a MCMCML method.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), York,UK, July 1999.
@INPROCEEDINGS{aj99b,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Hyperparameter estimation for satellite image restoration by a MCMCML method}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{York,UK}, |
| url |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-48432-9_9}, |
| keyword |
= |
{} |
| } |
|
162 - Simultaneous Image Classification and Restoration Using a Variational Approach.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Fort Collins, Colorado, USA, June 1999.
@INPROCEEDINGS{cs99c,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Simultaneous Image Classification and Restoration Using a Variational Approach}, |
| year |
= |
{1999}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Fort Collins, Colorado, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=784985}, |
| keyword |
= |
{} |
| } |
|
163 - Déconvolution d'images satellitaires: modèles et estimation de paramètres.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), Hammamet, Tunisie, March 1999.
@INPROCEEDINGS{aj99a,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Déconvolution d'images satellitaires: modèles et estimation de paramètres}, |
| year |
= |
{1999}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, |
| address |
= |
{Hammamet, Tunisie}, |
| pdf |
= |
{Articles/taima99.pdf}, |
| keyword |
= |
{} |
| } |
|
164 - Auxiliary functions and optimal scanning for road detection by dynamic programming.
N. Merlet and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Phoenix, USA, March 1999.
@INPROCEEDINGS{jz99d,
|
| author |
= |
{Merlet, N. and Zerubia, J.}, |
| title |
= |
{Auxiliary functions and optimal scanning for road detection by dynamic programming}, |
| year |
= |
{1999}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Phoenix, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=757554}, |
| keyword |
= |
{} |
| } |
|
165 - Qualification automatique des résultats d'une mise en correspondance de réseaux routiers en vue de la mise à jour cartographique.
C. Hivernat and S. Randriamasy and X. Descombes and J. Zerubia. In Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Paris, France, 1999.
@INPROCEEDINGS{xd99h,
|
| author |
= |
{Hivernat, C. and Randriamasy, S. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Qualification automatique des résultats d'une mise en correspondance de réseaux routiers en vue de la mise à jour cartographique}, |
| year |
= |
{1999}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing (ISPRS)}, |
| address |
= |
{Paris, France}, |
| keyword |
= |
{} |
| } |
|
166 - Classification et Restauration d'Images par Approche Variationnelle.
C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. In Proc. Journées des jeunes chercheurs en vision par ordinateur, Aussois, France, 1999.
@INPROCEEDINGS{cs99b,
|
| author |
= |
{Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, |
| title |
= |
{Classification et Restauration d'Images par Approche Variationnelle}, |
| year |
= |
{1999}, |
| booktitle |
= |
{Proc. Journées des jeunes chercheurs en vision par ordinateur}, |
| address |
= |
{Aussois, France}, |
| pdf |
= |
{Articles/orasis1999.pdf}, |
| keyword |
= |
{} |
| } |
|
167 - Image Retrieval and Indexing: A Hierarchical Approach in Computing the Distance between Textured Images.
R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Image Processing (ICIP), Chicago, USA, October 1998.
@INPROCEEDINGS{stoica98a,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{Image Retrieval and Indexing: A Hierarchical Approach in Computing the Distance between Textured Images}, |
| year |
= |
{1998}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=723675}, |
| keyword |
= |
{} |
| } |
|
168 - Unsupervised deconvolution of satellite images.
M. Khoumri and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Chicago, USA, October 1998.
@INPROCEEDINGS{lbf98d,
|
| author |
= |
{Khoumri, M. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Unsupervised deconvolution of satellite images}, |
| year |
= |
{1998}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=723322}, |
| keyword |
= |
{} |
| } |
|
169 - Segmentation of urban areas in SPOT images using MRF.
F. Richard and F. Falzon and J. Zerubia and G. Giraudon. In Proc. European Signal Processing Conference (EUSIPCO), Rhodes, September 1998.
@INPROCEEDINGS{jz98,
|
| author |
= |
{Richard, F. and Falzon, F. and Zerubia, J. and Giraudon, G.}, |
| title |
= |
{Segmentation of urban areas in SPOT images using MRF}, |
| year |
= |
{1998}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Rhodes}, |
| pdf |
= |
{http://www.eurasip.org/Proceedings/Eusipco/Eusipco1998/sessions/F%20A/FA%20P-7/2493/PAPER106.PDF}, |
| keyword |
= |
{} |
| } |
|
170 - Hyperparameter estimation of a variational model using a stochastic gradient.
J. Zerubia and L. Blanc-Féraud. In Proc. International Symposium on Optical Science, Engineering and Instrumentation, San Diego, USA, July 1998.
@INPROCEEDINGS{lbf98b,
|
| author |
= |
{Zerubia, J. and Blanc-Féraud, L.}, |
| title |
= |
{Hyperparameter estimation of a variational model using a stochastic gradient}, |
| year |
= |
{1998}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Symposium on Optical Science, Engineering and Instrumentation}, |
| address |
= |
{San Diego, USA}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=958908}, |
| keyword |
= |
{} |
| } |
|
171 - The two-dimensional Wold decomposition for segmentation and indexing in image libraries.
R. Stoica and J. Zerubia and J.M. Francos. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, May 1998.
@INPROCEEDINGS{stoica98b,
|
| author |
= |
{Stoica, R. and Zerubia, J. and Francos, J.M.}, |
| title |
= |
{The two-dimensional Wold decomposition for segmentation and indexing in image libraries}, |
| year |
= |
{1998}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Seattle, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=678151}, |
| keyword |
= |
{} |
| } |
|
172 - Denoising by extracting fractional order singularities.
H. Shekarforoush and J. Zerubia and M. Berthod. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, May 1998.
@INPROCEEDINGS{jz98a,
|
| author |
= |
{Shekarforoush, H. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Denoising by extracting fractional order singularities}, |
| year |
= |
{1998}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Seattle, USA}, |
| url |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=678129}, |
| keyword |
= |
{} |
| } |
|
173 - Fully Bayesian image segmentation-an engineering perspective.
R. Morris and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vol. 3, pages 54-57, Santa Barbara, CA, USA, October 1997.
Keywords : Bayes methods, Markov processes, Monte Carlo methods, Image sampling, Image segmentation.
@INPROCEEDINGS{MorrisICIP97,
|
| author |
= |
{Morris, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{ Fully Bayesian image segmentation-an engineering perspective}, |
| year |
= |
{1997}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| volume |
= |
{3}, |
| pages |
= |
{54-57}, |
| address |
= |
{Santa Barbara, CA, USA}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=631978&isnumber=13718}, |
| keyword |
= |
{Bayes methods, Markov processes, Monte Carlo methods, Image sampling, Image segmentation} |
| } |
Abstract :
Developments in Markov chain Monte Carlo procedures have made it possible to perform fully Bayesian image segmentation. By this we mean that all the parameters are treated identically, be they the segmentation labels, the class parameters or the Markov random field prior parameters. We perform the analysis by sampling from the posterior distribution of all the parameters. Sampling from the MRF parameters has traditionally been considered if not intractable then at least computationally prohibitive. In the statistics literature there are descriptions of experiments showing that the MRF parameters may be sampled by approximating the partition function. These experiments are all, however, on `toy' problems; for the typical size of image encountered in engineering applications the phase transition behaviour of the models becomes a major limiting factor in the estimation of the partition function. Nevertheless, we show that, with some care, fully Bayesian segmentation can be performed on realistic sized images. We also compare the fully Bayesian approach with the approximate pseudolikelihood method |
|
 top of the page
64 Technical and Research Reports |
1 - Classification of very high resolution SAR images of urban areas.
A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Rapport de recherche 7758, INRIA, October 2011.
Keywords : Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet.
@TECHREPORT{RR-7758,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas}, |
| year |
= |
{2011}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7758}, |
| url |
= |
{http://hal.inria.fr/docs/00/63/10/38/PDF/RR-7758.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Supervised classification, Bayesian, finite mixture models, hierarchical Markov random fields, wavelet} |
| } |
Résumé :
Dans le cadre d’une approche face aux risques environnementaux, nous proposons une nouvelle méthode de classification bayésienne supervisée. Celle-ci combine une modélisation statistique des images avec une prise en compte contextuelle via des champs de Markov hiérarchiques. Ce rapport de recherche vise à détailler plus amplement cette modélisation contextuelle, à savoir expliciter le modèle mathématique sur quad-arbre et l’obtention des observations par décomposition en ondelettes de l’image originale. Il met également en exergue certaines modifications apportées en
vue d’améliorer la classification finale. |
Abstract :
In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results. |
|
2 - On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation.
V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Research Report 7666, INRIA, July 2011.
Keywords : Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Synthetic Aperture Radar (SAR), Classification.
Copyright : INRIA/ARIANA
@TECHREPORT{RR-7666,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation}, |
| year |
= |
{2011}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7666}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00605274/en/}, |
| keyword |
= |
{Probability density function, Parameter estimation, generalized gamma distribution, K-distribution, Synthetic Aperture Radar (SAR), Classification} |
| } |
Résumé :
L'estimation de paramètres de fonctions de densité de probabilité est une étape majeure dans le domaine du traitement statistique du signal et des images. Dans ce rapport, nous étudions les propriétés et les limites de l'estimation de paramètres par la méthode des cumulants logarithmiques (MoLC), qui est une alternative à la fois au maximum de vraisemblance (MV) classique et à la méthode des moments. Nous dérivons la condition générale suffisante de consistance forte de l'estimation par la méthode MoLC, qui représente une propriété asymptotique importante de tout estimateur statistique. Grâce à cela, nous démontrons la consistance forte de l'estimation par la méthode MoLC pour une sélection de familles de distributions particulièrement adaptées (mais non restreintes) au traitement d'images acquises par radar à synthèse d'ouverture (RSO). Nous dérivons ensuite les conditions analytiques d'applicabilité de la méthode MoLC à des échantillons générés qui suivent les lois des différentes familles de distribution de notre sélection. Enfin, nous testons la méthode MoLC sur des données synthétiques et réelles afin de comparer les différentes propriétés inhérentes aux différents types d'images, l'applicabilité de la méthode et les effets d'un nombre restreint d'échantillons. Nous avons, en particulier, considéré les distributions gamma généralisée et K. Comme exemple d'application, nous avons réalisé des classifications supervisées d'images médicales à ultrason ainsi que d'images de télédétection acquises par des capteurs RSO. Les résultats obtenus montrent que la méthode MoLC est une bonne alternative à la méthode des moments, bien qu'elle contienne certaines limitations. Elle est particulièrement utile lorsqu'une approche directe par MV n'est pas possible. |
Abstract :
Parameter estimation of probability density functions is one of the major steps in the mainframe of statistical image and signal processing. In this report we explore the properties and limitations of the recently proposed method of logarithmic cumulants (MoLC) parameter estimation approach which is an alternative to the classical maximum likelihood (ML) and method of moments (MoM) approaches. We derive the general sufficient condition of strong consistency of MoLC estimates which represents an important asymptotic property of any statistical estimator. With its help we demonstrate the strong consistency of MoLC estimates for a selection of widely used distribution families originating (but not restricted to) synthetic aperture radar (SAR) image processing. We then derive the analytical conditions of applicability of MoLC to samples generated from several distribution families in our selection. Finally, we conduct various synthetic and real data experiments to assess the comparative properties, applicability and small sample performance of MoLC notably for the generalized gamma and K family of distributions. Supervised image classification experiments are considered for medical ultrasound and remote sensing SAR imagery. The obtained results suggest MoLC to be a feasible yet not universally applicable alternative to MoM that can be considered when the direct ML approach turns out to be unfeasible. |
|
3 - Unsupervised amplitude and texture based classification of SAR images with multinomial latent model.
K. Kayabol and J. Zerubia. Research Report 7700, INRIA, July 2011.
Keywords : High resolution SAR, Classification, Texture.
@TECHREPORT{Kayabol11,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{Unsupervised amplitude and texture based classification of SAR images with multinomial latent model}, |
| year |
= |
{2011}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7700}, |
| url |
= |
{http://hal.archives-ouvertes.fr/hal-00612491/fr/}, |
| keyword |
= |
{High resolution SAR, Classification, Texture} |
| } |
Abstract :
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes and a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error to model the textures of the classes. A non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. The Classification Expectation-Maximization (CEM) algorithm is performed to estimate the class parameters and to classify the pixels. We resort to Integrated Classification Likelihood (ICL) criterion to determine the number of classes in the model. We obtained some classification results of water, land and urban areas in both supervised and unsupervised cases on TerraSAR-X, as well as COSMO-SkyMed data.
|
|
4 - Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution .
S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. Rapport de recherche 7350, INRIA, July 2010.
Keywords : Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction.
@TECHREPORT{RR-7350,
|
| author |
= |
{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution }, |
| year |
= |
{2010}, |
| month |
= |
{July}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Rapport de recherche}, |
| number |
= |
{7350}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00508431/fr/}, |
| keyword |
= |
{Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction} |
| } |
|
5 - Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach.
C. Benedek and X. Descombes and J. Zerubia. Research Report 7143, INRIA, Sophia Antipolis, December 2009.
Keywords : Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics.
@TECHREPORT{benedekRR_09,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7143}, |
| address |
= |
{Sophia Antipolis}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00426615}, |
| keyword |
= |
{Change detection, Building extraction, Marked point process, MAP, multiple birth-and-death dynamics} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle méthode probabiliste qui intègre l'extraction de bâtiments et la détection de changements à partir de paires d'images de télédétection. Un algorithme d'optimisation globale permet de trouver la configuration optimale de bâtiments en considérant des observations, des connaissances a priori et des interactions entre des parties voisines de bâtiments. La précision est assurée par une vérification d'un modèle objet bayésien; le coût du calcul est considérablement réduit en utilisant un processus stochastique non-uniforme de naissance d'objets fondé sur des caractéristiques bas-niveaux des images, qui génère des objets pertinents ayant une grande probabilité. |
Abstract :
In this report we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features. |
|
6 - Space non-invariant point-spread function and its estimation in fluorescence microscopy.
P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. Research Report 7157, INRIA, December 2009.
Keywords : Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy.
@TECHREPORT{ppankajakshan09c,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Space non-invariant point-spread function and its estimation in fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7157}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00438719/en/}, |
| keyword |
= |
{Confocal Laser Scanning Microscopy, point spread function, Bayesian estimation, MAP estimation, Deconvolution, fluorescence microscopy} |
| } |
Résumé :
Dans ce rapport de recherche, nous rappelons brièvement comment la nature limitée de diffraction de l'objectif d'un microscope optique, et le bruit
intrinsèque peuvent affecter la résolution d'une image observée. Un algorithme de déconvolution aveugle a été proposé en vue de restaurer les fréquences manquants au delà de la limite de diffraction. Cependant, sous d'autres conditions, l'approximation du systéme imageur l'imagerie sans aberration n'est plus valide et donc les aberrations de la phase du front d'onde émergeant d'un médium ne sont plus ignorées. Dans la deuxième partie de
ce rapport de recherche, nous montrons que la distribution d'intensité originelle et la localisation d'un objet peuvent être retrouvées uniquement en obtenant de la phase du front d'onde
réfracté, à partir d'images d'intensité observées. Nous démontrons cela par obtention de la fonction de ou a partir d'une microsphère imagée. Le bruit et l'infl uence de la taille de la
microsphère peuvent être diminués et parfois complètement supprimes des images observées en utilisant un estimateur maximum a posteriori. Néanmoins, a cause de l'incohérence du système d'acquisition, une récupération de phase a partir d'intensités observées n'est possible que si la restauration de la phase est contrainte. Nous avons utilisé l'optique géométrique
pour modéliser la phase du front d'onde réfracté, et nous avons teste l'algorithme sur des images simulées. |
Abstract :
In this research report, we recall briefly how the diffraction-limited nature of an optical microscope's objective, and the intrinsic noise can affect the observed images' resolution. A blind deconvolution algorithm can restore the lost frequencies beyond the diffraction limit. However, under other imaging conditions, the approximation of aberration-free imaging, is not applicable, and the phase aberrations of the emerging wavefront from a specimen immersion medium cannot be ignored any more. We show that an object's location and its original intensity distribution can be recovered by retrieving the refracted wavefront's phase from the observed intensity images. We demonstrate this by retrieving the point-spread function from an imaged microsphere. The noise and the influence of the microsphere size can be mitigated and sometimes completely removed from the observed images by using a maximum a posteriori estimate. However, due to the incoherent nature of the acquisition system, phase retrieval from the observed intensities will be possible only if the phase is constrained. We have used geometrical optics to model the phase of the refracted wavefront, and tested the algorithm on some simulated images. |
|
7 - High resolution SAR-image classification.
V. Krylov and J. Zerubia. Research Report 7108, INRIA, November 2009.
Keywords : SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula.
Copyright : INRIA/ARIANA, 2009
@TECHREPORT{RR-7108,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{High resolution SAR-image classification}, |
| year |
= |
{2009}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{7108}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00433036/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/44/81/40/PDF/RR-7108.pdf}, |
| keyword |
= |
{SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula} |
| } |
Résumé :
Dans ce rapport, nous proposons une nouvelle approche pour la classification des images de type Radar à Synthèse d’Ouverture (RSO) haute résolution. Cette approche combine la méthode des champs Markoviens (MRF) pour la classification bayésienne et un modèle de mélange fini pour l’estimation des densités de probabilité. Ce modèle de mélange fini est realisé grace à une approche fondée sur une espérance-maximisation stochastique, à partir d'un dictionnaire, pour l’estimation des densités de probabilité d’amplitude. Cette approche semi-automatique est étendue au cas important des images RSO avec plusieurs polarisations, en utilisant des copulas pour modéliser les distributions jointes. Des résultats expérimentaux, sur plusieurs images RSO réelles (Dual-Pol TerraSAR-X et Single-Pol COSMO-SkyMed), pour la classification de zones humides, sont présentés pour montrer l’efficacité de l’algorithme proposé. |
Abstract :
In this report we propose a novel classification algorithm for high and very high resolution synthetic aperture radar (SAR) amplitude images that combines the Markov random field approach to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done by dictionary-based stochastic expectation maximization amplitude histogram estimation approach. The developed semiautomatic algorithm is extended to an important case of multi-polarized SAR by modeling the joint distributions of channels via copulas. The accuracy of the proposed algorithm is validated for the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed. |
|
8 - Modeling the statistics of high resolution SAR images.
V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. Research Report 6722, INRIA, November 2008.
Keywords : Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM).
Copyright : INRIA/ARIANA, 2008
@TECHREPORT{krylovDSEM08,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Modeling the statistics of high resolution SAR images}, |
| year |
= |
{2008}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6722}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00342681/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/35/76/27/PDF/RR-6722.pdf}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR) image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM)} |
| } |
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 pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images. |
|
9 - Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept.
P. Pankajakshan and L. Blanc-Féraud and B. Zhang and Z. Kam and J.C. Olivo-Marin and J. Zerubia. Research Report 6493, INRIA, April 2008.
Keywords : Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Total variation.
Copyright : ARIANA/INRIA
@TECHREPORT{ppankajakshan08b,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Zhang, B. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Parametric blind deconvolution for confocal laser scanning microscopy-proof of concept}, |
| year |
= |
{2008}, |
| month |
= |
{April}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6493}, |
| url |
= |
{https://hal.inria.fr/inria-00269265}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/27/02/92/PDF/report.pdf}, |
| keyword |
= |
{Confocal Laser Scanning Microscopy, Bayesian restoration, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, Total variation} |
| } |
Résumé :
Nous proposons une méthode de restauration itérative d’images de fluorescence
CLSM et d’estimation paramétrique de la fonction de flou (PSF) du système d’acquisition.
Le CLSM est un microscope qui balaye un échantillon en 3D et utilise une sténopée pour
rejeter la lumière en dehors du point de focalisation. Néanmoins, la qualité des images
souffre de deux limitations physiques. La première est due à la diffraction due au système
optique et la seconde est due à la quantité réduite de lumière détectée par le tube
photo-multiplicateur (PMT). Ces limitations induisent respectivement un flou et du bruit
de comptage de photons. Les images peuvent alors bénéficier d’un post-traitement de
restauration fondé sur la déconvolution. Le problème à traiter est l’estimation simultanée
de la distribution 3D de l’échantillon des sources fluorescentes et de la PSF du microscope
(i.e. de déconvolution aveugle). En utilisant un modèle de processus physique
d’acquisition d’images microscopiques (CLSM), on réduit le nombre de paramètres libres
décrivant la PSF et on introduit des contraintes. On introduit aussi des connaissances a
priori sur l’échantillon ce qui permet de stabiliser le processus d’estimation et de favoriser
la convergence. Des expériences sur des données synthétiques montrent que la PSF peut
être estimée avec précision. Des expériences sur des données réelles montrent de bons
resultats de déconvolution en comparaison avec le modèle théorique de la PSF du microscope. |
Abstract :
We propose a method for the iterative restoration of fluorescence Confocal Laser Scanning Microscope (CLSM) images with parametric estimation of the acquisition system’s Point Spread Function (PSF). The CLSM is an optical fluorescence microscope that scans a specimen in 3D and uses a pinhole to reject most of the out-of-focus light. However, the quality of the image suffers from two primary physical limitations. The first is due to the diffraction-limited nature of the optical system and the second is due to the reduced amount of light detected by the photomultiplier tube (PMT). These limitations cause blur and photon counting noise respectively. The images can hence benefit from post-processing restoration methods based on deconvolution. An efficient method for parametric blind image deconvolution involves the simultaneous estimation of the specimen 3D distribution of fluorescent sources and the microscope PSF. By using a model for the microscope image acquisition physical process, we reduce the number of free parameters describing the PSF and introduce constraints. The parameters of the PSF may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We also introduce a priori knowledge of the specimen that permits stabilization of the estimation process and favorizes the convergence. Experiments on simulated data show that the PSF could be estimatedwith a higher degree of accuracy and those done on real data show very good deconvolution results in comparison to the theoretical microscope PSF model. |
|
10 - Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier.
M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. Research Report 6732, INRIA, 2008.
Keywords : l1 norm, nesterov scheme, total variation minimization, wavelet.
Copyright :
@TECHREPORT{RR-6732,
|
| author |
= |
{Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Reconstruction d'images satellitaires à partir d'un échantillonnage irrégulier}, |
| year |
= |
{2008}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6732}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00340975/fr/}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/34/09/75/PDF/RR-6732.pdf}, |
| keyword |
= |
{l1 norm, nesterov scheme, total variation minimization, wavelet} |
| } |
|
11 - Support Vector Machines for burnt area discrimination.
O. Zammit and X. Descombes and J. Zerubia. Research Report 6343, INRIA, November 2007.
Keywords : Forest fires, Burnt areas, Satellite images, Support Vector Machines, Classification.
@TECHREPORT{zammit_RR_07,
|
| author |
= |
{Zammit, O. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Support Vector Machines for burnt area discrimination}, |
| year |
= |
{2007}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6343}, |
| url |
= |
{http://hal.inria.fr/inria-00185101/fr/}, |
| pdf |
= |
{http://hal.inria.fr/inria-00185101/fr/}, |
| keyword |
= |
{Forest fires, Burnt areas, Satellite images, Support Vector Machines, Classification} |
| } |
Résumé :
Ce rapport aborde le problème de l'évaluation des dégâts après un feux de forêt. La détection est effectuée à partir d'une seule image satellite (SPOT 5) acquise après le feu. Afin de détecter les zones brûlées, nous utilisons une approche récente de classification nommée SVM (Séparateurs à Vaste Marge). Cette méthode est comparée aux algorithmes de classification plus conventionnels comme les K-moyennes ou les K-plus proches voisins, qui sont régulièrement utilisés en traitement d'image. Nous proposons également une méthode de classification non supervisée combinant les K-moyennes et les SVM. Les résultats fournis par les différentes techniques sont comparés à des vérités de terrain sur diverses zones brûlées. |
Abstract :
This report addresses the problem of burnt area discrimination using remote sensing images. The detection is based on a single post-fire image acquired by SPOT 5 satellite. To delineate the burnt areas, we use a recent classification method called Support Vectors Machines (SVM). This approach is compared to more conventional classifiers such as K-means or K-nearest neighbours which are widely used in image processing. We also proposed a new automatic classification approach combining K-means and SVM. The results given by the different methods are finally compared to ground truths on various burnt areas |
|
12 - Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations.
S. Descamps and X. Descombes and A. Béchet and J. Zerubia. Research Report 6328, INRIA, October 2007.
Keywords : Object extraction, modélisation stochastique , Marked point process, dynamique de naissance/mort, environnement, flamants roses.
@TECHREPORT{Descamps-Descombes,
|
| author |
= |
{Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, |
| title |
= |
{Détection de flamants roses par processus ponctuels marqués pour l'estimation de la taille des populations}, |
| year |
= |
{2007}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6328}, |
| url |
= |
{http://hal.inria.fr/inria-00180811}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/18/08/93/PDF/RR-Desc-Desc-Bech-Zeru.pdf}, |
| keyword |
= |
{Object extraction, modélisation stochastique , Marked point process, dynamique de naissance/mort, environnement, flamants roses} |
| } |
|
13 - An adaptive simulated annealing cooling schedule for object detection in images.
M. Ortner and X. Descombes and J. Zerubia. Research Report 6336, INRIA, October 2007.
Keywords : Image procressing, Shape extraction, Spatial point process, Simulated Annealing, Adaptive cooling schedule.
@TECHREPORT{Ortner-Descombes,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{An adaptive simulated annealing cooling schedule for object detection in images}, |
| year |
= |
{2007}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6336}, |
| url |
= |
{https://hal.inria.fr/inria-00181764}, |
| pdf |
= |
{https://hal.inria.fr/inria-00181764}, |
| keyword |
= |
{Image procressing, Shape extraction, Spatial point process, Simulated Annealing, Adaptive cooling schedule} |
| } |
|
14 - A Three-layer MRF model for Object Motion Detection in Airborne Images.
C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. Research Report 6208, INRIA, June 2007.
Keywords : Aerial images, Change detection, Camera motion, MRF.
@TECHREPORT{benedek_INRIARR07,
|
| author |
= |
{Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{A Three-layer MRF model for Object Motion Detection in Airborne Images}, |
| year |
= |
{2007}, |
| month |
= |
{June}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6208}, |
| url |
= |
{https://hal.inria.fr/inria-00150805}, |
| pdf |
= |
{https://hal.inria.fr/inria-00150805}, |
| keyword |
= |
{Aerial images, Change detection, Camera motion, MRF} |
| } |
|
15 - Hierarchical finite-state modeling for texture segmentation with application to forest classification.
G. Scarpa and M. Haindl and J. Zerubia. Research Report 6066, INRIA, INRIA, France, December 2006.
Keywords : Texture, Segmentation, Co-occurrence matrix, Structural approach, MCMC, Synthesis.
@TECHREPORT{scarparr06,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{Hierarchical finite-state modeling for texture segmentation with application to forest classification}, |
| year |
= |
{2006}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6066}, |
| address |
= |
{INRIA, France}, |
| url |
= |
{https://hal.inria.fr/inria-00118420}, |
| keyword |
= |
{Texture, Segmentation, Co-occurrence matrix, Structural approach, MCMC, Synthesis} |
| } |
Abstract :
In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- and inter-texture interactions, and eventually addresses the segmentation task in a completely unsupervised manner. Moreover, it provides a hierarchical output, as the user may decide the scale at which the segmentation has to be given. Tests were carried out on both natural texture mosaics provided by the Prague Texture Segmentation Datagenerator Benchmark and remote-sensing data of forest areas provided by the French National Forest Inventory (IFN). |
|
16 - A higher-order active contour model of a `gas of circles' and its application to tree crown extraction.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. Research Report 6026, INRIA, France, November 2006.
Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape.
@TECHREPORT{Horvath05,
|
| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{A higher-order active contour model of a `gas of circles' and its application to tree crown extraction}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6026}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00115631}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath05.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} |
| } |
Abstract :
Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, \eg medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques. |
|
17 - A structural approach for 3D building reconstruction.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 6048, INRIA, November 2006.
Keywords : 3D reconstruction, Structural approach, Building, RJMCMC, Viterbi.
@TECHREPORT{Lafarge_rr_6048,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A structural approach for 3D building reconstruction}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6048}, |
| url |
= |
{https://hal.inria.fr/inria-00114338}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Lafarge_rr_6048.pdf}, |
| keyword |
= |
{3D reconstruction, Structural approach, Building, RJMCMC, Viterbi} |
| } |
|
18 - An automatic building extraction method : Application to the 3D-city modeling.
F. Lafarge and P. Trontin and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5925, INRIA, France, May 2006.
Keywords : Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM).
@TECHREPORT{lafarge_rr_may06,
|
| author |
= |
{Lafarge, F. and Trontin, P. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An automatic building extraction method : Application to the 3D-city modeling}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5925}, |
| address |
= |
{France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_rr_may06.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM)} |
| } |
|
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