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Publications de type 'inproceedings'
Résultat de la recherche dans la liste des publications :
245 Articles de conférence |
1 - Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test.
V. Krylov et G. Moser et A. Voisin et S.B. Serpico et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Orlando, United States, octobre 2012.
@INPROCEEDINGS{ICIP12,
|
| author |
= |
{Krylov, V. and Moser, G. and Voisin, A. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test}, |
| year |
= |
{2012}, |
| month |
= |
{octobre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Orlando, United States}, |
| url |
= |
{http://hal.inria.fr/hal-00724284}, |
| keyword |
= |
{} |
| } |
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2 - Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model.
A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans EURASIP, Bucarest, Romania, août 2012.
@INPROCEEDINGS{EURASIP12,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model}, |
| year |
= |
{2012}, |
| month |
= |
{août}, |
| booktitle |
= |
{EURASIP}, |
| address |
= |
{Bucarest, Romania}, |
| url |
= |
{http://hal.inria.fr/hal-00723286}, |
| keyword |
= |
{} |
| } |
|
3 - A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction.
K. Kayabol et J. Zerubia. Dans IEEE International Geoscience and Remote Sensing Symposium, pages 4054-4057, Munich, Germany, juillet 2012.
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4 - An hierarchical approach for model-based classification of SAR images.
K. Kayabol et J. Zerubia. Dans 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, avril 2012.
@INPROCEEDINGS{Kayabol12,
|
| author |
= |
{Kayabol, K. and Zerubia, J.}, |
| title |
= |
{An hierarchical approach for model-based classification of SAR images}, |
| year |
= |
{2012}, |
| month |
= |
{avril}, |
| booktitle |
= |
{20th Signal Processing and Communications Applications Conference}, |
| address |
= |
{Mugla, Turkey}, |
| url |
= |
{http://hal.inria.fr/hal-00686658}, |
| keyword |
= |
{} |
| } |
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5 - Multichannel hierarchical image classification using multivariate copulas.
A. Voisin et V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans IS&T/SPIE Electronic Imaging – Computational Imaging X, San Francisco, United States, janvier 2012.
@INPROCEEDINGS{SPIE12,
|
| author |
= |
{Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Multichannel hierarchical image classification using multivariate copulas}, |
| year |
= |
{2012}, |
| month |
= |
{janvier}, |
| booktitle |
= |
{IS&T/SPIE Electronic Imaging – Computational Imaging X}, |
| address |
= |
{San Francisco, United States}, |
| url |
= |
{http://dx.doi.org/10.1117/12.917298}, |
| keyword |
= |
{} |
| } |
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6 - Building large urban environments from unstructured point data.
F. Lafarge et C. Mallet. Dans Proc. IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, novembre 2011.
@INPROCEEDINGS{lafarge_iccv11,
|
| author |
= |
{Lafarge, F. and Mallet, C.}, |
| title |
= |
{Building large urban environments from unstructured point data}, |
| year |
= |
{2011}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Barcelona, Spain}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126353}, |
| keyword |
= |
{} |
| } |
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7 - Synthetic Aperture Radar Image Classification via Mixture Approaches.
V. Krylov et J. Zerubia. Dans Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, novembre 2011.
Mots-clés : Radar a Ouverture Synthetique (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution.
Copyright : IEEE
@INPROCEEDINGS{krylovCOMCAS11,
|
| author |
= |
{Krylov, V. and Zerubia, J.}, |
| title |
= |
{Synthetic Aperture Radar Image Classification via Mixture Approaches}, |
| year |
= |
{2011}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, |
| address |
= |
{Tel Aviv, Israel}, |
| url |
= |
{http://www.ortra.biz/comcas/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00625551/en/}, |
| keyword |
= |
{Radar a Ouverture Synthetique (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} |
| } |
Abstract :
In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors. |
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8 - Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil.
J. Zhou et C. Proisy et X. Descombes et J. Zerubia et G. Le Maire et Y. Nouvellon et P. Couteron. Dans Asian Conference on Pattern Recognition (ACPR), Beijing, China, novembre 2011.
Mots-clés : tree detection, Eucalyptus plantation, Marked point process, multi-date detection.
@INPROCEEDINGS{Zhou11,
|
| author |
= |
{Zhou, J. and Proisy, C. and Descombes, X. and Zerubia, J. and Le Maire, G. and Nouvellon, Y. and Couteron, P.}, |
| title |
= |
{Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil}, |
| year |
= |
{2011}, |
| month |
= |
{novembre}, |
| booktitle |
= |
{Asian Conference on Pattern Recognition (ACPR)}, |
| address |
= |
{Beijing, China}, |
| url |
= |
{http://hal.inria.fr/hal-00740973}, |
| keyword |
= |
{tree detection, Eucalyptus plantation, Marked point process, multi-date detection} |
| } |
Abstract :
Individual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations. |
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9 - Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images.
S. Prigent et D. Zugaj et X. Descombes et P. Martel et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
@INPROCEEDINGS{prigent11a,
|
| author |
= |
{Prigent, S. and Zugaj, D. and Descombes, X. and Martel, P. and Zerubia, J.}, |
| title |
= |
{Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/59/06/94/PDF/icip_final.pdf}, |
| keyword |
= |
{} |
| } |
Abstract :
Clinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy. |
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10 - Two constrained formulations for deblurring Poisson noisy images.
M. Carlavan et L. Blanc-Féraud. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
Mots-clés : Poisson deconvolution, discrepancy principle, constrained convex optimization.
@INPROCEEDINGS{ICIP2011_Carlavan,
|
| author |
= |
{Carlavan, M. and Blanc-Féraud, L.}, |
| title |
= |
{Two constrained formulations for deblurring Poisson noisy images}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00591035/fr/}, |
| keyword |
= |
{Poisson deconvolution, discrepancy principle, constrained convex optimization} |
| } |
Abstract :
Deblurring noisy Poisson images has recently been subject of an increasingly amount of works in many areas such as astronomy or biological imaging. Several methods have promoted explicit prior on the solution to regularize the ill-posed inverse problem and to improve the quality of the image. In each of these methods, a regularizing parameter is introduced to control the weight of the prior. Unfortunately, this regularizing parameter has to be manually set such that it gives the best qualitative results. To tackle this issue, we present in this paper two constrained formulations for the Poisson deconvolution problem, derived from recent advances in regularizing parameter estimation for Poisson noise. We first show how to improve the accuracy of these estimators and how to link these estimators to constrained formulations. We then propose an algorithm to solve the resulting optimization problems and detail how to perform the projections on the constraints. Results on real and synthetic data are presented. |
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Rapport d'activité 2011
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