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Publications de type 'inproceedings'
Résultat de la recherche dans la liste des publications :
245 Articles de conférence |
131 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, septembre 2005.
@INPROCEEDINGS{lacoste_eusipco05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, |
| keyword |
= |
{} |
| } |
|
132 - Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods.
S. Le Hegarat-Mascle et A. Kallel et X. Descombes. Dans Proc. SPIE Symposium on Remote Sensing, Vol. 5982, Bruges, Belgium, septembre 2005.
@INPROCEEDINGS{mascle_spie_05,
|
| author |
= |
{Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, |
| title |
= |
{Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| volume |
= |
{5982}, |
| address |
= |
{Bruges, Belgium}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=879756}, |
| keyword |
= |
{} |
| } |
|
133 - Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction.
F. Lafarge et X. Descombes et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, septembre 2005.
Mots-clés : Support Vector Machines, Base d'apprentissage, Champs de Markov, Feux de foret, Zones urbaines.
Copyright : IEEE
@INPROCEEDINGS{lafarge_icip05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_icip05.pdf}, |
| keyword |
= |
{Support Vector Machines, Base d'apprentissage, Champs de Markov, Feux de foret, Zones urbaines} |
| } |
|
134 - Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model.
A. Achim et E.E. Kuruoglu et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, septembre 2005.
@INPROCEEDINGS{achim_eusipco_05,
|
| author |
= |
{Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| pdf |
= |
{http://kilyos.ee.bilkent.edu.tr/~signal/defevent/papers/cr1741.pdf}, |
| keyword |
= |
{} |
| } |
|
135 - Texture-adaptive mother wavelet selection for texture analysis.
G.C.K. Abhayaratne et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, septembre 2005.
Mots-clés : Texture, Paquet d'ondelettes, Adaptatif, Mere.
@INPROCEEDINGS{abhayaratne_icip05,
|
| author |
= |
{Abhayaratne, G.C.K. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture-adaptive mother wavelet selection for texture analysis}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Abhayaratne05icip.pdf}, |
| keyword |
= |
{Texture, Paquet d'ondelettes, Adaptatif, Mere} |
| } |
Abstract :
Classification results obtained using wavelet-based texture analysis techniques vary with the choice of mother wavelet used in the methodology. We discuss the use of mother wavelet filters as parameters in a probabilistic approach to texture analysis based on adaptive biorthogonal wavelet packet bases. The optimal choice for the mother wavelet filters is estimated from the data, in addition to the other model parameters. The model is applied to the classification of single texture images and mosaics of Brodatz textures, the results showing improvement over the performance of standard wavelets for a given filter length. |
|
136 - A Marked Point Process Model for Tree Crown Extraction in Plantations.
G. Perrin et X. Descombes et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, septembre 2005.
Mots-clés : Geometrie stochastique, RJMCMC, Extraction de Houppiers, Extraction d'objets, Processus ponctuels marques.
@INPROCEEDINGS{perrin_icip05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process Model for Tree Crown Extraction in Plantations}, |
| year |
= |
{2005}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.ps.gz}, |
| keyword |
= |
{Geometrie stochastique, RJMCMC, Extraction de Houppiers, Extraction d'objets, Processus ponctuels marques} |
| } |
Abstract :
This work presents a framework to extract tree crowns from remotely sensed data, especially in plantation images, using stochastic geometry. We aim at finding the tree top positions, and the tree crown diameter distribution. Our approach consists in considering that these images are some realizations of a marked point process. First we model the tree plantation as a configuration of an unknown number of ellipses. Then, a Bayesian energy is defined, containing both a prior energy which incorporates the prior knowledge of the plantation geometric properties, and a likelihood which fits the objects to the data. Eventually, we estimate the global minimum of this energy using Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing scheme. We present results on optical aerial images of poplars provided by IFN. |
|
137 - Shape Moments for Region-Based Active Contours.
P. Horvath et A. Bhattacharya et I. H. Jermyn et J. Zerubia et Z. Kato. Dans Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition, Szeged, Hungary, mai 2005.
@INPROCEEDINGS{horvath_hacippr05,
|
| author |
= |
{Horvath, P. and Bhattacharya, A. and Jermyn, I. H. and Zerubia, J. and Kato, Z.}, |
| title |
= |
{Shape Moments for Region-Based Active Contours}, |
| year |
= |
{2005}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition}, |
| address |
= |
{Szeged, Hungary}, |
| url |
= |
{http://vision.vein.hu/HACIPPR/}, |
| keyword |
= |
{} |
| } |
|
138 - A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform.
G. Pons Bernad et L. Blanc-Féraud et J. Zerubia. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Pennsylvania, USA, mars 2005.
@INPROCEEDINGS{pons_icassp2005,
|
| author |
= |
{Pons Bernad, G. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform}, |
| year |
= |
{2005}, |
| month |
= |
{mars}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Philadelphia, Pennsylvania, USA}, |
| pdf |
= |
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1415481}, |
| keyword |
= |
{} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and increasingly popular technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise due to photon-limited detection. Several deconvolution and/or denoising methods have been proposed to reduce these degradations.Here we propose a wavelet denoising method, which turns out to be very effective for three-dimensional confocal images. To obtain a translation and rotation invariant algorithm, we have developped the 3D Complex Wavelet Transform introduced by N. Kingsbury. These wavelets allow moreover a better directional selectivity of the wavelet coefficients. We show on simulated and real biological data the good performances of this algorithm. |
|
139 - Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory.
R. Cossu et I. H. Jermyn et J. Zerubia. Dans Proc. Physics in Signal and Image Processing, Toulouse, France, janvier 2005.
Mots-clés : Bimodale, Statistics, Paquet d'ondelettes, Adaptatif, Texture.
@INPROCEEDINGS{cossu_psip05,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory}, |
| year |
= |
{2005}, |
| month |
= |
{janvier}, |
| booktitle |
= |
{Proc. Physics in Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, |
| keyword |
= |
{Bimodale, Statistics, Paquet d'ondelettes, Adaptatif, Texture} |
| } |
Abstract :
In recent work, it was noted that although the subband histograms
for standard wavelet coefcients take on a generalized
Gaussian form, this is no longer true for wavelet packet
bases adapted to a given texture. Instead, three types of subband
statistics are observed: Gaussian, generalized Gaussian,
and interestingly, in some subbands, bi- or multi-modal histograms.
Motivated by this observation, we provide additional
experimental conrmation of the existence of multimodal
subbands, and provide a theoretical explanation for
their occurrence. The results reveal the connection of such
subbands with the characteristic structure in a texture, and
thus confirm the importance of such subbands for image modelling
and applications. |
|
140 - Texture discrimination using multimodal wavelet packet subbands.
R. Cossu et I. H. Jermyn et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Singapore, octobre 2004.
Mots-clés : Bimodale, Adaptatif, probabilistic, Paquet d'ondelettes, Texture.
@INPROCEEDINGS{cossu_icip04,
|
| author |
= |
{Cossu, R. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Texture discrimination using multimodal wavelet packet subbands}, |
| year |
= |
{2004}, |
| month |
= |
{octobre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Singapore}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu04icip.pdf}, |
| keyword |
= |
{Bimodale, Adaptatif, probabilistic, Paquet d'ondelettes, Texture} |
| } |
Abstract :
The subband histograms of wavelet packet bases adapted to individual
texture classes often fail to display the leptokurtotic behaviour
shown by the standard wavelet coefcients of `natural'
images. While many subband histograms remain leptokurtotic
in adaptive bases, some subbands are Gaussian. Most interestingly,
however, some subbands show multimodal behaviour, with
no mode at zero. In this paper, we provide evidence for the existence
of these multimodal subbands and show that they correspond
to narrow frequency bands running throughout images of the texture.
They are thus closely linked to the texture's structure. As
such, they seem likely to possess superior descriptive and discriminative
power as compared to unimodal subbands. We demonstrate
this using both Brodatz and remote sensing images. |
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