|
Publications de type 'inproceedings'
Résultat de la recherche dans la liste des publications :
245 Articles de conférence |
61 - Fast Realization of Digital Elevation Model . X. Descombes et A. Kraushonak et P. Lukashevish et B. Zalessky. Dans PRIP , pages 156-160, Minsk, Belarus, mai 2009.
@INPROCEEDINGS{KRA-09,
|
author |
= |
{Descombes, X. and Kraushonak, A. and Lukashevish, P. and Zalessky, B.}, |
title |
= |
{Fast Realization of Digital Elevation Model }, |
year |
= |
{2009}, |
month |
= |
{mai}, |
booktitle |
= |
{PRIP }, |
pages |
= |
{156-160}, |
address |
= |
{Minsk, Belarus}, |
url |
= |
{http://www.iapr.org/members/newsletter/Newsletter09-03/index_files/Page420.htm}, |
pdf |
= |
{https://hal.inria.fr/inria-00423678/document}, |
keyword |
= |
{} |
} |
|
62 - Smoothing techniques for convex problems. Applications in image processing. P. Weiss et M. Carlavan et L. Blanc-Féraud et J. Zerubia. Dans Proc. SAMPTA (international conference on Sampling Theory and Applications), Marseille, France, mai 2009. Mots-clés : nesterov scheme, convergence rate, Dual smoothing.
@INPROCEEDINGS{PWEISS_SAMPTA09,
|
author |
= |
{Weiss, P. and Carlavan, M. and Blanc-Féraud, L. and Zerubia, J.}, |
title |
= |
{Smoothing techniques for convex problems. Applications in image processing}, |
year |
= |
{2009}, |
month |
= |
{mai}, |
booktitle |
= |
{Proc. SAMPTA (international conference on Sampling Theory and Applications)}, |
address |
= |
{Marseille, France}, |
url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, |
pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Sampta09.pdf}, |
keyword |
= |
{nesterov scheme, convergence rate, Dual smoothing} |
} |
Abstract :
In this paper, we present two algorithms to solve some inverse problems coming from the field of image processing. The problems we study are convex and can be expressed simply as sums of lp-norms of affine transforms of the image. We propose 2 different techniques. They are - to the best of our knowledge - new in the domain of image processing and one of them is new in the domain of mathematical programming. Both methods converge to the set of minimizers. Additionally, we show that they converge at least as O(1/N) (where N is the iteration counter) which is in some sense an ``optimal'' rate of convergence. Finally, we compare these approaches to some others on a toy problem of image super-resolution with impulse noise. |
|
63 - Dictionary-based probability density function estimation for high-resolution SAR data. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. Dans Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009), Vol. 7246, pages 72460S, San Jose, USA, janvier 2009. Mots-clés : SAR image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (SEM). Copyright : SPIE
@INPROCEEDINGS{KrylovSPIE09,
|
author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
title |
= |
{Dictionary-based probability density function estimation for high-resolution SAR data}, |
year |
= |
{2009}, |
month |
= |
{janvier}, |
booktitle |
= |
{Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009)}, |
volume |
= |
{7246}, |
pages |
= |
{72460S}, |
address |
= |
{San Jose, USA}, |
url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=812524}, |
pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00361384/en/}, |
keyword |
= |
{SAR image, Probability density function, parametric estimation, finite mixture models, EM Stochastique (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 the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) 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). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution 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 accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes. |
|
64 - Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images. A. El Ghoul et I. H. Jermyn et J. Zerubia. Dans International Conference on Pattern Recognition (ICPR), Tampa, Florida, décembre 2008. Mots-clés : Phase diagram, Higher-order actif contours, Forme, river extraction.
@INPROCEEDINGS{ElGhoul08b,
|
author |
= |
{El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, |
title |
= |
{Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images}, |
year |
= |
{2008}, |
month |
= |
{décembre}, |
booktitle |
= |
{International Conference on Pattern Recognition (ICPR)}, |
address |
= |
{Tampa, Florida}, |
url |
= |
{https://hal.inria.fr/inria-00316619}, |
pdf |
= |
{http://hal.inria.fr/docs/00/31/66/19/PDF/icpr08aymenelghoul.pdf}, |
keyword |
= |
{Phase diagram, Higher-order actif contours, Forme, river extraction} |
} |
Abstract :
The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. Higher-order active contours
provide a way to include such knowledge, but their behaviour can vary significantly with parameter values: e.g. the same energy can model networks or a ‘gas of circles’. In this paper, we present a stability analysis
of a HOAC energy leading to the phase diagram of a long bar. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of network shapes using the energy.
We apply the resulting model to the problem of hydrographic network extraction from VHR satellite images. |
|
65 - A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences. C. Benedek et T. Szirányi. Dans Proc. International Conference on Pattern Recognition (ICPR), Tampa, USA, décembre 2008. Mots-clés : Aerial images, Change detection, mixed Markov models.
@INPROCEEDINGS{benedekICPR08,
|
author |
= |
{Benedek, C. and Szirányi, T.}, |
title |
= |
{A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences}, |
year |
= |
{2008}, |
month |
= |
{décembre}, |
booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
address |
= |
{Tampa, USA}, |
pdf |
= |
{http://hal.inria.fr/docs/00/35/91/16/PDF/benedekICPR08.pdf}, |
keyword |
= |
{Aerial images, Change detection, mixed Markov models} |
} |
Abstract :
In the paper we propose a novel multi-layer Mixed Markov model for detecting relevant changes in registered aerial images taken with significant time differences. The introduced approach combines global intensity statistics with local correlation and contrast features. A global energy optimization process simultaneously ensures optimal local feature selection and smooth, observation-consistent classification. Validation is given on real aerial photos. |
|
66 - A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas. A. Fournier et P. Weiss et L. Blanc-Féraud et G. Aubert. Dans International Conference on Pattern Recognition (ICPR), Tampa, USA, décembre 2008. Mots-clés : Change detection, Level Lines, remote sensing. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@INPROCEEDINGS{l_lines_icpr08,
|
author |
= |
{Fournier, A. and Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, |
title |
= |
{A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas}, |
year |
= |
{2008}, |
month |
= |
{décembre}, |
booktitle |
= |
{International Conference on Pattern Recognition (ICPR)}, |
address |
= |
{Tampa, USA}, |
url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, |
pdf |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, |
keyword |
= |
{Change detection, Level Lines, remote sensing} |
} |
|
67 - Texture representation by geometric objects using a jump-diffusion process. F. Lafarge et G. Gimel'farb. Dans Proc. British Machine Vision Conference (BMVC), Leeds, U.K., novembre 2008.
@INPROCEEDINGS{lafarge_bmvc08,
|
author |
= |
{Lafarge, F. and Gimel'farb, G.}, |
title |
= |
{Texture representation by geometric objects using a jump-diffusion process}, |
year |
= |
{2008}, |
month |
= |
{novembre}, |
booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
address |
= |
{Leeds, U.K.}, |
url |
= |
{http://www.comp.leeds.ac.uk/bmvc2008/proceedings/papers/86.pdf}, |
keyword |
= |
{} |
} |
|
68 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images. T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. European Conference on Computer Vision (ECCV), Marseille, France, octobre 2008. Mots-clés : Dense urban area, Champ de Phase, Reseaux routiers, Methodes variationnelles, Very high resolution. Copyright :
@INPROCEEDINGS{Peng08c,
|
author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
title |
= |
{An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images}, |
year |
= |
{2008}, |
month |
= |
{octobre}, |
booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
address |
= |
{Marseille, France}, |
pdf |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-88690-7_38}, |
keyword |
= |
{Dense urban area, Champ de Phase, Reseaux routiers, Methodes variationnelles, Very high resolution} |
} |
Abstract :
This paper addresses the segmentation from an image of entities that have the form of a 'network', i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present a new phase field higher-order active contour (HOAC) prior model for network regions, and apply it to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much 'noise' in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a severe limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. To solve this problem, we propose a new HOAC prior energy term, and reformulate it as a nonlocal phase field energy. We analyse the stability of the new model, and find that in addition to solving the above problem by separating the interactions between points on the same and opposite sides of a network branch, the new model permits the modelling of two widths
simultaneously. The analysis also fixes some of the model parameters in terms of network width(s). After adding a likelihood energy, we use the model to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The results demonstrate the superiority of the new model, the importance of strong prior knowledge in general, and of the new term in particular. |
|
69 - A Geometric Primitive Extraction Process for Remote Sensing Problems.. F. Lafarge et G. Gimel'farb et X. Descombes. Dans Proc. Advanced Concepts for Intelligent Vision Systems, pages 518-529, Juan-les-Pins, France, octobre 2008. Copyright :
@INPROCEEDINGS{LGF2008,
|
author |
= |
{Lafarge, F. and Gimel'farb, G. and Descombes, X.}, |
title |
= |
{A Geometric Primitive Extraction Process for Remote Sensing Problems.}, |
year |
= |
{2008}, |
month |
= |
{octobre}, |
booktitle |
= |
{ACIVS}, |
pages |
= |
{518-529}, |
address |
= |
{Juan-les-Pins, France}, |
pdf |
= |
{http://www.springerlink.com/content/b228321527177226/}, |
keyword |
= |
{} |
} |
|
70 - Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas. O. Zammit et X. Descombes et J. Zerubia. Dans Proc. International Conference on Pattern Recognition and Image Analysis (PRIA), Nizhny Novgorod, Russia, septembre 2008. Mots-clés : Classification, Segmentation, Support Vector Machines, Zones brûlées, Feux de foret, Imagerie satellitaire. Copyright :
@INPROCEEDINGS{zammit_pria_08,
|
author |
= |
{Zammit, O. and Descombes, X. and Zerubia, J.}, |
title |
= |
{Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas}, |
year |
= |
{2008}, |
month |
= |
{septembre}, |
booktitle |
= |
{Proc. International Conference on Pattern Recognition and Image Analysis (PRIA)}, |
address |
= |
{Nizhny Novgorod, Russia}, |
pdf |
= |
{http://hal.inria.fr/inria-00316297/fr/}, |
keyword |
= |
{Classification, Segmentation, Support Vector Machines, Zones brûlées, Feux de foret, Imagerie satellitaire} |
} |
|
haut de la page
Ces pages sont générées par
|