|
|
Publications of 2005
Result of the query in the list of publications :
9 Articles |
1 - Detecting codimension-two objects in an image with Ginzburg-Landau models.
G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005.
Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images.
@ARTICLE{laure-ijcv05,
|
| author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
| title |
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{Detecting codimension-two objects in an image with Ginzburg-Landau models}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{65}, |
| number |
= |
{1-2}, |
| pages |
= |
{29-42}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, |
| keyword |
= |
{Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} |
| } |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from
Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its
mathematical properties and give some experimental results demonstrating its capability in image processing. |
|
2 - Point Processes for Unsupervised Line Network Extraction in Remote Sensing.
C. Lacoste and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, October 2005.
@ARTICLE{lacoste05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point Processes for Unsupervised Line Network Extraction in Remote Sensing}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{27}, |
| number |
= |
{10}, |
| pages |
= |
{1568-1579}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=32189&arnumber=1498752&count=18&index=4}, |
| keyword |
= |
{} |
| } |
|
3 - Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model.
G. Poggi and G. Scarpa and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 43(8): pages 1901-1911, August 2005.
Keywords : Classification, Segmentation, Markov Fields.
@ARTICLE{ieeetgrs_05,
|
| author |
= |
{Poggi, G. and Scarpa, G. and Zerubia, J.}, |
| title |
= |
{Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model}, |
| year |
= |
{2005}, |
| month |
= |
{August}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{43}, |
| number |
= |
{8}, |
| pages |
= |
{1901-1911}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/32001/01487647.pdf?tp=&arnumber=1487647&isnumber=32001}, |
| keyword |
= |
{Classification, Segmentation, Markov Fields} |
| } |
|
4 - Dual Norms and Image Decomposition Models.
J.F. Aujol and A. Chambolle. International Journal of Computer Vision, 63(1): pages 85-104, June 2005.
Keywords : Image decomposition.
@ARTICLE{AujolChambolle,
|
| author |
= |
{Aujol, J.F. and Chambolle, A.}, |
| title |
= |
{Dual Norms and Image Decomposition Models}, |
| year |
= |
{2005}, |
| month |
= |
{June}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{63}, |
| number |
= |
{1}, |
| pages |
= |
{85-104}, |
| pdf |
= |
{http://link.springer.com/article/10.1007/s11263-005-4948-3}, |
| keyword |
= |
{Image decomposition} |
| } |
|
5 - Invariant Bayesian estimation on manifolds.
I. H. Jermyn. Annals of Statistics, 33(2): pages 583--605, April 2005.
Keywords : Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's.
@ARTICLE{jermyn_annstat05,
|
| author |
= |
{Jermyn, I. H.}, |
| title |
= |
{Invariant Bayesian estimation on manifolds}, |
| year |
= |
{2005}, |
| month |
= |
{April}, |
| journal |
= |
{Annals of Statistics}, |
| volume |
= |
{33}, |
| number |
= |
{2}, |
| pages |
= |
{583--605}, |
| url |
= |
{http://dx.doi.org/10.1214/009053604000001273}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_annstat05.pdf}, |
| keyword |
= |
{Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's} |
| } |
Abstract :
A frequent and well-founded criticism of the maximum em a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter param taking values in a differentiable manifold paramspace is that they are not invariant to arbitrary `reparametrizations' of paramspace. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a em sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and then provides a solution. We first show that the presence of a metric structure on paramspace can be used to define coordinate-invariant MAP and MMSE estimates, and we argue that this is the natural way to proceed. We then discuss the choice of a metric structure on paramspace. By imposing an invariance criterion natural within a Bayesian framework, we show that this choice is essentially unique. It does not necessarily correspond to a choice of coordinates. In cases of complete prior ignorance, when Jeffreys' prior is used, the invariant MAP estimate reduces to the maximum likelihood estimate. The invariant MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation. |
|
6 - Modeling very Oscillating Signals. Application to Image Processing.
G. Aubert and J.F. Aujol. Applied Mathematics and Optimization, 51(2): pages 163--182, March 2005.
@ARTICLE{AujolAubert,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Modeling very Oscillating Signals. Application to Image Processing}, |
| year |
= |
{2005}, |
| month |
= |
{March}, |
| journal |
= |
{Applied Mathematics and Optimization}, |
| volume |
= |
{51}, |
| number |
= |
{2}, |
| pages |
= |
{163--182}, |
| pdf |
= |
{http://link.springer.com/article/10.1007/s00245-004-0812-z}, |
| keyword |
= |
{} |
| } |
|
7 - Optimal Partitions, Regularized Solutions, and Application to Image Classification.
G. Aubert and J.F. Aujol. Applicable Analysis, 84(1): pages 15--35, January 2005.
@ARTICLE{AujolAubertclassif,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Optimal Partitions, Regularized Solutions, and Application to Image Classification}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| journal |
= |
{Applicable Analysis}, |
| volume |
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{84}, |
| number |
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{1}, |
| pages |
= |
{15--35}, |
| pdf |
= |
{http://www.math.u-bordeaux1.fr/~jaujol/HDR/A2.pdf}, |
| keyword |
= |
{} |
| } |
|
8 - Image Decomposition into a Bounded Variation Component and an Oscillating Component.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. Journal of Mathematical Imaging and Vision, 22(1): pages 71--88, January 2005.
@ARTICLE{BLA05,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Image Decomposition into a Bounded Variation Component and an Oscillating Component}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| journal |
= |
{Journal of Mathematical Imaging and Vision}, |
| volume |
= |
{22}, |
| number |
= |
{1}, |
| pages |
= |
{71--88}, |
| pdf |
= |
{http://link.springer.com/article/10.1007/s10851-005-4783-8}, |
| keyword |
= |
{} |
| } |
|
9 - Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 180: pages 4--12, 2005.
Keywords : 3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images.
Copyright : SFPT
@ARTICLE{lafarge_sfpt05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution}, |
| year |
= |
{2005}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| volume |
= |
{180}, |
| pages |
= |
{4--12}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_sfpt05.pdf}, |
| keyword |
= |
{3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images} |
| } |
|
 top of the page
2 PhD Thesis and Habilitations |
1 - Constance de largeur et désocclusion dans les images digitales.
E. Villéger. PhD Thesis, Universite de Nice Sophia Antipolis, December 2005.
@PHDTHESIS{villeger_these,
|
| author |
= |
{Villéger, E.}, |
| title |
= |
{Constance de largeur et désocclusion dans les images digitales}, |
| year |
= |
{2005}, |
| month |
= |
{December}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| pdf |
= |
{ http://www-sop.inria.fr/dias/Theses/phd-12.php}, |
| keyword |
= |
{} |
| } |
|
2 - Contours actifs d'ordre supérieur et leur application à la détection de linéiques dans des images de télédétection.
M. Rochery. PhD Thesis, Universite de Nice Sophia Antipolis, Sophia Antipolis, September 2005.
Keywords : Active contour, Higher-order, Phase Field, Line networks, Road network.
@PHDTHESIS{rochery_these,
|
| author |
= |
{Rochery, M.}, |
| title |
= |
{Contours actifs d'ordre supérieur et leur application à la détection de linéiques dans des images de télédétection}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| address |
= |
{Sophia Antipolis}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/04/86/28/PDF/tel-00010631.pdf}, |
| keyword |
= |
{Active contour, Higher-order, Phase Field, Line networks, Road network} |
| } |
|
top of the page
13 Conference articles |
1 - Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), St Augustine, Florida, USA, November 2005.
Keywords : Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC.
Copyright : Springer Verlag
@INPROCEEDINGS{perrin_emmcvpr05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, |
| address |
= |
{St Augustine, Florida, USA}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.pdf}, |
| ps |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.ps.gz}, |
| keyword |
= |
{Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC} |
| } |
Abstract :
We use marked point processes to detect an unknown number of trees from high resolution aerial images. This is in fact an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects to the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects.
We compare here different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels. |
|
2 - Phase field models and higher-order active contours.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Computer Vision (ICCV), Beijing, China, October 2005.
Keywords : Active contour, Higher-order, Shape, Line networks, Road network, Phase Field.
@INPROCEEDINGS{rochery_iccv05,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Phase field models and higher-order active contours}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Computer Vision (ICCV)}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_iccv05.pdf}, |
| keyword |
= |
{Active contour, Higher-order, Shape, Line networks, Road network, Phase Field} |
| } |
Abstract :
The representation and modelling of regions is an important topic in computer vision. In this paper, we represent a region via a level set of a `phase field' function. The function is not constrained, eg to be a distance function; nevertheless, phase field energies equivalent to classical active contour energies can be defined. They represent an advantageous alternative to other methods: a linear representation space; ease of implementation (a PDE with no reinitialization); neutral initialization; greater topological freedom. We extend the basic phase field model with terms that reproduce `higher-order active contour' energies, a powerful way of including prior geometric knowledge in the active contour framework via nonlocal interactions between contour points. In addition to the above advantages, the phase field greatly simplifies the analysis and implementation of the higher-order terms. We define a phase field model that favours regions composed of thin arms meeting at junctions, combine this with image terms, and apply the model to the extraction of line networks from remote sensing images. |
|
3 - Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares.
F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu-Marni. In Proc. GRETSI Symposium on Signal and Image Processing, Louvain-la-Neuve, Belgique, September 2005.
Keywords : Rare event, Forest fires, Gaussian Field.
@INPROCEEDINGS{lafarge_gretsi05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu-Marni, S.}, |
| title |
= |
{Détection de feux de forêt à partir d'images satellitaires IRT par analyse statistique d'évènements rares}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
= |
{Louvain-la-Neuve, Belgique}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_gretsi05.pdf}, |
| keyword |
= |
{Rare event, Forest fires, Gaussian Field} |
| } |
|
4 - New Higher-order Active Contour Energies for Network Extraction.
M. Rochery and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Gap closure, Shape, Prior, Higher-order, Active contour.
@INPROCEEDINGS{rochery_icip05,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{New Higher-order Active Contour Energies for Network Extraction}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Genoa, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/rochery_icip05.pdf}, |
| keyword |
= |
{Gap closure, Shape, Prior, Higher-order, Active contour} |
| } |
Abstract :
Using the framework of higher-order active contours, we present a new quadratic em continuation energy for the extraction of line networks (e.g. road, hydrographic, vascular) in the presence of occlusions. Occlusions create gaps in the data that frequently translate to gaps in the extracted network. The new energy penalizes earby opposing extremities of the network, and thus favours the closure of the gaps created by occlusions. Nearby opposing extremities are identified using a
sophisticated interaction between pairs of points on the contour. This new model allows the extraction of fully connected networks, even though occlusions violate common assumptions about the homogeneity of the
interior, and high contrast with the exterior, of the network. We present experimental results on real aerial images that demonstrate the effectiveness of the new model for network extraction tasks. |
|
5 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 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 |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, |
| keyword |
= |
{} |
| } |
|
6 - Application of ant colony optimization to image classification using a Markov model withnonstationary neighborhoods.
S. Le Hegarat-Mascle and A. Kallel and X. Descombes. In Proc. SPIE Symposium on Remote Sensing, Vol. 5982, Bruges, Belgium, September 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 |
= |
{September}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| volume |
= |
{5982}, |
| address |
= |
{Bruges, Belgium}, |
| url |
= |
{http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=879756}, |
| keyword |
= |
{} |
| } |
|
7 - Textural Kernel for SVM Classification in Remote Sensing : Application to Forest Fire Detection and Urban Area Extraction.
F. Lafarge and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Support Vector Machines, Learning base, Markov Fields, Forest fires, Urban areas.
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 |
= |
{September}, |
| 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, Learning base, Markov Fields, Forest fires, Urban areas} |
| } |
|
8 - Maximum A Posteriori Estimation of Radar Cross Section in SAR Images using the Heavy-Tailed Rayleigh Model.
A. Achim and E.E. Kuruoglu and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 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 |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Antalya, Turkey}, |
| pdf |
= |
{http://kilyos.ee.bilkent.edu.tr/~signal/defevent/papers/cr1741.pdf}, |
| keyword |
= |
{} |
| } |
|
9 - Texture-adaptive mother wavelet selection for texture analysis.
G.C.K. Abhayaratne and I. H. Jermyn and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Texture, Wavelet packet, Adaptive, Mother.
@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 |
= |
{September}, |
| 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, Wavelet packet, Adaptive, Mother} |
| } |
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. |
|
10 - A Marked Point Process Model for Tree Crown Extraction in Plantations.
G. Perrin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005.
Keywords : Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process.
@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 |
= |
{September}, |
| 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 |
= |
{Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process} |
| } |
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. |
|
11 - Shape Moments for Region-Based Active Contours.
P. Horvath and A. Bhattacharya and I. H. Jermyn and J. Zerubia and Z. Kato. In Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition, Szeged, Hungary, May 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 |
= |
{May}, |
| booktitle |
= |
{Proc. Hungarian-Austrian Conference on Image Processing and Pattern Recognition}, |
| address |
= |
{Szeged, Hungary}, |
| url |
= |
{http://vision.vein.hu/HACIPPR/}, |
| keyword |
= |
{} |
| } |
|
12 - A Restoration Method for Confocal Microscopy Using Complex Wavelet Transform.
G. Pons Bernad and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Philadelphia, Pennsylvania, USA, March 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 |
= |
{March}, |
| 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. |
|
13 - Multimodal statistics of adaptive wavelet packet coefficients: experimental evidence and theory.
R. Cossu and I. H. Jermyn and J. Zerubia. In Proc. Physics in Signal and Image Processing, Toulouse, France, January 2005.
Keywords : Bimodal, Statistics, Wavelet packet, Adaptive, 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 |
= |
{January}, |
| booktitle |
= |
{Proc. Physics in Signal and Image Processing}, |
| address |
= |
{Toulouse, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Cossu05psip.pdf}, |
| keyword |
= |
{Bimodal, Statistics, Wavelet packet, Adaptive, 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. |
|
top of the page
10 Technical and Research Reports |
1 - Higher-Order Active Contour Energies for Gap Closure.
M. Rochery and I. H. Jermyn and J. Zerubia. Research Report 5717, INRIA, France, October 2005.
Keywords : Road network, Continuity, Gap closure, Higher-order, Active contour, Shape.
@TECHREPORT{RR_5717,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
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{Higher-Order Active Contour Energies for Gap Closure}, |
| year |
= |
{2005}, |
| month |
= |
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= |
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= |
{Research Report}, |
| number |
= |
{5717}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070300/fr/}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70300/filename/RR-5717.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/03/00/PS/RR-5717.ps}, |
| keyword |
= |
{Road network, Continuity, Gap closure, Higher-order, Active contour, Shape} |
| } |
Résumé :
L'un des principaux problèmes lors de l'extraction de réseaux
linéiques dans des images, et en particulier l'extraction de réseaux
routiers dans des images de télédétection, est l'existence d'interruptions
dans les données, causées, par exemple, par des occultations. Ces
interruptions peuvent mener à des trous dans le réseau extrait qui
n'existent pas dans le réseau réel. Dans ce rapport, nous décrivons une
énergie de contour actif d'ordre supérieur qui, en plus de favoriser les
régions composées de bras fins et connectés entre eux, inclut un terme d'a
priori qui pénalise les configurations du réseau où des extremités proches
et se faisant face apparaissent. L'apparition dans le réseau extrait de ces
configurations est donc moins probable. Si des extremités proches et se
faisant face apparaissent pendant l'évolution par descente de gradient
utilisée pour minimiser l'énergie, le nouveau terme dans l'énergie crée une
attraction entre ces extremités, qui se rapprochent donc l'une de l'autre
et se rejoignent, fermant ainsi le trou entre elles. Pour minimiser
l'énergie, nous développons des techniques spécifiques pour traiter les
derivées d'ordre élevé qui apparaissent dans l'équation de descente de
gradient. Nous présentons des résultats d'extraction automatique de réseaux
routiers à partir d'images de télédétection, montrant ainsi la capacité du
modèle à surmonter les interruptions. |
Abstract :
One of the main difficulties in extracting line networks from
images, and in particular road networks from remote sensing images, is the
existence of interruptions in the data caused, for example, by occlusions.
These can lead to gaps in the extracted network that do not correspond to
gaps in the real network. In this report, we describe a higher-order active
contour energy that in addition to favouring network-like regions composed
of thin arms joining at junctions, also includes a prior term that
penalizes network configurations containing `nearby opposing extremities',
and thereby makes their appearance in the extracted network less likely. If
nearby opposing extremities form during the gradient descent evolution used
to minimize the energy, the new energy term causes the extremities to
attract one another, and hence to move towards one another and join, thus
closing the gap. To minimize the energy, we develop specific techniques to
handle the high-order derivatives that appear in the gradient descent
equation. We present the results of automatic extraction of networks from
real remote-sensing images, showing the ability of the model to overcome
interruptions. |
|
2 - A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models..
M. Ortner and X. Descombes and J. Zerubia. Research Report 5712, INRIA, France, October 2005.
Keywords : Marked point process, Buildings, RJMCMC.
@TECHREPORT{ortner-RR05,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models.}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5712}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070305}, |
| keyword |
= |
{Marked point process, Buildings, RJMCMC} |
| } |
Résumé :
Ce travail présente une approche par géométrie stochastique pour l'extraction de primitives dans les images. Ces structures sont modélisées sous forme de réalisations d'un processus ponctuel spatial marqué dont les points sont des formes géométriques. Cette approche permet d'incorporer un modèle a priori sur la répartition spatiale des structures d'intérêt. Plus spécifiquement, nous présentons un modèle fondé sur l'interaction d'un processus de rectangles avec un processus de segments. Le premier est dédié à la détection des zones homogènes dans l'image et le second à la détection des discontinuités significatives. Nous définissons l'énergie d'une configuration de façon à favoriser la connection entre les segments, l'alignement des rectangles et l'adéquation entre les deux types de primitives. L'estimation repose sur l'emploi d'une technique de recuit-simulé. Le modèle proposé est appliqué à l'analyse de Modèles Numériques d'Elevation. Nous présentons des résultats sur des données réelles fournies par l'Institut Géographique National (IGN). Nous montrons en particulier que l'approche est efficace sur des données de types très différents. |
Abstract :
A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models.
This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a prior knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types. |
|
3 - A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5687, INRIA, France, September 2005.
Keywords : 3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM).
@TECHREPORT{5687,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5687}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070326/fr/}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70326/filename/RR-5687.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/03/26/PS/RR-5687.ps}, |
| keyword |
= |
{3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM)} |
| } |
Résumé :
Dans ce rapport, nous développons un modèle paramétrique pour la reconstruction automatique de bâtiments en 3D fondé sur une approche bayésienne à partir de simulations PLEIADES. Les images satellitaires haute résolution représentent un nouveau type de données permettant de traiter les problèmes de reconstruction 3D de bâtiments. Leur résolution ``relativement basse'' et leur faible rapport signal sur bruit pour ce type de problèmes ne permet pas l'utilisation des méthodes standard développées dans le cas des images aériennes. Nous proposons une approche paramétrique utilisant des Modèles Numériques d'Elévation (MNE) et les empreintes de bâtiments associées modélisées par rectangles. La méthode proposée est fondée sur une approche bayésienne. Une technique de type de Monte Carlo par Chaînes de Markov est utilisée afin d'optimiser le modèle énergétique. |
Abstract :
This report develops a parametric model for automatic 3D building reconstruction based on a Bayesian approach from PLEIADES simulations. High resolution satellite images are a new kind of data to deal with 3D building reconstruction problems. Their ``relatively low'' resolution and low signal noise ration do not allow to use standard methods developed for the aerial image case. We propose a parametric approach using Digital Elevation Models (DEM) and associated rectangular building footprints. The proposed method is based on a Bayesian approach. A Markov Chain Monte Carlo technique is used to optimize the energy model. |
|
4 - Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. Research Report 5697, INRIA, France, September 2005.
@TECHREPORT{rrHimne,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
= |
{Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5697}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070318}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/07/03/18/PDF/RR-5697.pdf}, |
| keyword |
= |
{} |
| } |
Résumé :
Ce rapport présente un algorithme d'extraction non supervisée de réseaux hydrographiques à partir d'images satellitaires exploitant la structure arborescante de tels réseaux. L'extraction du surfacique (branches de largeur supérieure à trois pixels) est réalisée par un algorithme efficace fondé sur une modélisation par champ de Markov. Ensuite, l'extraction du linéique se fait par un algorithme récursif fondé sur un modèle hiérarchique dans lequel les affluents d'un fleuve donné sont modélisés par un processus ponctuel marqué défini dans le voisinage de ce fleuve. L'optimisation de chaque processus ponctuel est réalisée par un recuit simulé utilisant un algorithme de Monte Carlo par chaîne de Markov à sauts réversibles. Nous obtenons de bons résultats en terme d'omissions et de surdétections sur une image radar de type ERS. |
Abstract :
This report presents a two-step algorithm for unsupervised extraction of hydrographic networks from satellite images, that exploits the tree structures of such networks. First, the thick branches of the network are detected by an efficient algorithm based on a Markov random field. Second, the line branches are extracted using a recursive algorithm based on a hierarchical model of the hydrographic network, in which the tributaries of a given river are modeled by an object process (or a marked point process) defined within the neighborhood of this river. Optimization of each point process is done via simulated annealing using a reversible jump Markov chain Monte Carlo algorithm. We obtain encouraging results in terms of omissions and overdetections on a radar satellite image. |
|
5 - A Polyline Process for Unsupervised Line Network Extraction in Remote Sensing.
C. Lacoste and X. Descombes and J. Zerubia. Research Report 5698, INRIA, France, September 2005.
@TECHREPORT{rrCaroline,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Polyline Process for Unsupervised Line Network Extraction in Remote Sensing}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5698}, |
| address |
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{France}, |
| url |
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{http://hal.inria.fr/inria-00070317}, |
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{http://hal.inria.fr/docs/00/07/03/17/PDF/RR-5698.pdf}, |
| ps |
= |
{http://hal.inria.fr/docs/00/07/03/17/PS/RR-5698.ps}, |
| keyword |
= |
{} |
| } |
Résumé :
Ce rapport présente un nouveau modèle issu de la géométrie stochastique pour l'extraction non supervisée de réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires ou aériennes. Le réseau linéique présent dans la scène observée est modélisé par un processus de lignes brisées, appelé CAROLINE. Le modèle a priori incorpore de fortes contraintes géométriques et topologiques au travers de potentiels sur la forme des lignes brisées et de potentiels d'interaction. Les propriétés radiométriques sont incorporées via la construction d'un terme d'attache aux données fondé sur des tests statistiques. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) à sauts réversibles permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation. L'ajout de perturbations pertinentes permet une accélération de la convergence de l'algorithme. Des résultats expérimentaux obtenus sur des images satellitaires et aériennes sont présentés et comparés à ceux obtenus avec un précédent modèle fondé sur un processus de segments, appelé Quality Candy. |
Abstract :
This report presents a new stochastic geometry model for unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. The line network in the observed scene is modeled by a polyline process, named CAROLINE. The prior model incorporates strong geometrical and topological constraints through potentials on the polyline shape and interaction potentials. Data properties are taken into account through a data term based on statistical tests. Optimization is done via a simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Experimental results are provided on aerial and satellite images and compared with the results obtained with a previous model, that is a segment process called Quality Candy. |
|
6 - Optimization Techniques for Energy Minimization Problem in a Marked Point Process Application to Forestry.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5704, INRIA, France, September 2005.
Keywords : Simulated Annealing, Marked point process, Stochastic geometry, Optimization.
@TECHREPORT{rr_perrin_optim_05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Optimization Techniques for Energy Minimization Problem in a Marked Point Process Application to Forestry}, |
| year |
= |
{2005}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5704}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070312}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70312/filename/RR-5704.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/03/12/PS/RR-5704.ps}, |
| keyword |
= |
{Simulated Annealing, Marked point process, Stochastic geometry, Optimization} |
| } |
Résumé :
Dans ce rapport de recherche, nous utilisons les processus ponctuels marqués afin d'extraire un nombre inconnu d'objets dans des images aériennes. Ces processus sont définis par une énergie, qui contient un terme a priori formalisant les interactions entre objets ainsi qu'un terme d'attache aux données. Nous cherchons à minimiser cette énergie, afin d'obtenir la meilleure configuration d'objets, à l'aide d'un recuit simulé qui s'inscrit dans l'algorithme d'échantillonnage MCMC à sauts réversibles.
Nous comparons ici différents schémas de décroissance de température, et présentons certaines méthodes qui permettent d'améliorer la convergence de l'algorithme en un temps fini. |
Abstract :
We use marked point processes to detect an unknown number of trees from high resolution aerial images. This approach turns to be an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects onto the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects.
We compare in this paper different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels. |
|
7 - Higher Order Active Contours.
M. Rochery and I. H. Jermyn and J. Zerubia. Research Report 5656, INRIA, France, August 2005.
Keywords : Active contour, Higher-order, Road network, Shape, Prior.
@TECHREPORT{RR_5656,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Higher Order Active Contours}, |
| year |
= |
{2005}, |
| month |
= |
{August}, |
| institution |
= |
{INRIA}, |
| type |
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{Research Report}, |
| number |
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{https://hal.inria.fr/inria-00070352}, |
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| ps |
= |
{https://hal.inria.fr/docs/00/07/03/52/PS/RR-5656.ps}, |
| keyword |
= |
{Active contour, Higher-order, Road network, Shape, Prior} |
| } |
Résumé :
Nous introduisons une nouvelle classe de contours actifs qui offre des perspectives intéressantes pour la modélisation des régions et des formes, et nous appliquons un cas particulier de ces modèles à l'extraction de réseaux linéiques dans des images satellitaires et aériennes. Les nouveaux modèles sont des fonctionnelles polynômiales arbitraires sur l'espace des contours, et généralisent ainsi les fonctionnelles linéaires utilisées dans les modèles classiques de contours actifs. Alors que les fonctionnelles classiques s'écrivent avec de simples intégrales sur le contour, les nouvelles énergies sont définies comme des intégrales multiples, décrivant ainsi des interactions de longue portée entre les différents ensembles de points du contour. Utilisées comme des termes d'a priori, les fonctionnelles décrivent des familles de contours aux propriétés géométriques complexes, sans faire référence à une forme spécifique et sans nécessiter l'estimation de la position. Utilisées comme des termes d'attache aux données, elles permettent de décrire des interactions multi-points entre le contour et les données. Afin de minimiser ces énergies, nous adoptons la méthodologie des courbes de niveau. Les forces dérivées des énergies sont cependant non locales, et nécessitent une extension des méthodes de courbes de niveau standard. Les réseaux sont une famille de formes d'une grande importance dans de nombreuses applications et en particulier en télédétection. Pour les modéliser, nous faisons un choix particulier d'énergie quadratique qui décrit des structures branchées et nous ajoutons un terme d'attache aux données qui lie les données et la géométrie du contour au niveau des paires de points du contour. Des résultats d'extraction prometteurs sont montrés sur des images réelles. |
Abstract :
We introduce a new class of active contour models that hold great promise for region and shape modelling, and we apply a special case of these models to the extraction of road networks from satellite and aerial imagery. The new models are arbitrary polynomial functionals on the space of boundaries, and thus greatly generalize the linear functionals used in classical contour energies. While classical energies are expressed as single integrals over the contour, the new energies incorporate multiple integrals, and thus describe long-range interactions between different sets of contour points. As prior terms, they describe families of contours that share complex geometric properties, without making reference to any particular shape, and they require no pose estimation. As likelihood terms, they can describe multi-point interactions between the contour and the data. To optimize the energies, we use a level set approach. The forces derived from the new energies are non-local however, thus necessitating an extension of standard level set methods. Networks are a shape family of great importance in a number of applications, including remote sensing imagery. To model them, we make a particular choice of prior quadratic energy that describes reticulated structures, and augment it with a likelihood term that couples the data at pairs of contour points to their joint geometry. Promising experimental results are shown on real images. |
|
8 - Point Processes in Forestry : an Application to Tree Crown Detection.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5544, INRIA, France, April 2005.
Keywords : Marked point process, Object extraction, RJMCMC, Tree Crown Extraction, Stochastic geometry.
@TECHREPORT{5544,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point Processes in Forestry : an Application to Tree Crown Detection}, |
| year |
= |
{2005}, |
| month |
= |
{April}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5544}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070463}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70463/filename/RR-5544.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/04/63/PS/RR-5544.ps}, |
| keyword |
= |
{Marked point process, Object extraction, RJMCMC, Tree Crown Extraction, Stochastic geometry} |
| } |
Résumé :
Dans ce rapport de recherche, notre but est d'extraire des houppiers à partir d'images aériennes de forêts à l'aide de processus ponctuels marqués de disques et d'ellipses. Notre approche consiste, en effet, à modéliser les données comme des réalisations de tels processus. Une fois l'objet géométrique de référence choisi, nous échantillonnons le processus objet défini par une densité grâce à un algorithme MCMC à sauts réversibles, optimisé par un recuit simulé afin d'extraire le maximum a posteriori de cette densité. Cette configuration optimale nous donnera l'extraction recherchée.
Dans une première partie, nous proposons de revenir quelque peu sur les processus ponctuels marqués et leur application dans la foresterie. Puis, nous présentons deux nouveaux modèles d'extraction de houppiers à base de disques et d'ellipses, et discutons de quelques améliorations au niveau de la simulation et de l'optimisation de notre algorithme.
Nous présentons des résultats obtenus sur des images aériennes très haute résolution fournies par l'Inventaire Forestier National (IFN), ainsi que sur des images synthétiques simulées avec le logiciel AMAP (Bionatics, projet Digiplante). |
Abstract :
In this research report, we aim at extracting tree crowns from remotely sensed images using marked point processes of discs and ellipses. Our approach is indeed to consider that the data are some realizations of a marked point process. Once a geometrical object is defined, we sample a marked point process defined by a density with a Reversible Jump Markov Chain Monte Carlo dynamics and simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image.
In a first part, we propose to review the basis of marked point processes and some of their examples used in forestry statistic inference. Then, we present two new models, with discs and ellipses, and discuss some improvements made in the optimization or in the simulation.
Results are shown on high resolution aerial images of poplars provided by the French Forest Inventory (IFN), and synthetic images simulated with AMAP software (Bionatics, Digiplante project). |
|
9 - Restauration d'Images Biologiques 3D en Microscopie Confocale par Transformée en Ondelettes Complexes.
G. Pons Bernad and L. Blanc-Féraud and J. Zerubia. Research Report 5507, INRIA, France, February 2005.
Keywords : Confocal microscopy, Complex 3D Wavelet Transform, Restoration, Denoising, Deconvolution.
@TECHREPORT{5507,
|
| author |
= |
{Pons Bernad, G. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{Restauration d'Images Biologiques 3D en Microscopie Confocale par Transformée en Ondelettes Complexes}, |
| year |
= |
{2005}, |
| month |
= |
{February}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5507}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070500}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70500/filename/RR-5507.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/05/00/PS/RR-5507.ps}, |
| keyword |
= |
{Confocal microscopy, Complex 3D Wavelet Transform, Restoration, Denoising, Deconvolution} |
| } |
Résumé :
La microscopie confocale est une méthode puissante pour l'imagerie 3D de spécimens biologiques. Néanmoins, les images acquises sont dégradées non seulement par du flou dû à la lumière provenant de zones non focalisées du spécimen, mais aussi par un bruit de Poisson dû à la détection. Plusieurs algorithmes de déconvolution ont été proposés pour réduire ces dégradations. Un des plus utilisés est l'algorithme itératif de Richardson-Lucy, qui calcule un maximum de vraisemblance adapté à une statistique poissonienne. Mais cet algorithme tend à amplifier le bruit. Une solution consiste alors à introduire une contrainte de régularisation (par exemple, fondée sur la Variation Totale). Ici, nous nous concentrons sur des méthodes fondées sur l'analyse par ondelettes, en particulier sur des méthodes de débruitage via la transformée en ondelettes, qui semblent être plus appropriées à la microscopie en fluorescence 3D. Nous développons dans ce rapport un algorithme de Transformation en Ondelettes Complexes 3D introduit par N. Kingsbury. Celui-ci permet une décomposition invariante par translation et rotation et une sélectivité directionnelle des coefficients en ondelettes. Nous montrons sur des images synthétiques et sur des images réelles les résultats de cet algorithme de débruitage. Ce dernier est ensuite inséré dans le processus de déconvolution. |
Abstract :
Confocal laser scanning microscopy is a powerful technique for 3D imaging of biological specimens. However the acquired images are degraded by blur from out-of-focus light and Poisson noise. Several deconvolution algorithms have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. Nevertheless, this algorithm tends to amplify noise. Other solutions exist which combine Richardson-Lucy algorithm and regularization (for example with a Total Variation constraint). In this report, we will concentrate on methods based on wavelet analysis, in particular on wavelet denoising methods, which turn out to be very effective in application to 3D confocal images. To obtain a translation and rotation invariant decomposition algorithm, we have developped the 3D Complex Wavelet Transform introduced by Nick Kingsbury. These wavelets allow moreover a directional selectivity of the wavelet coefficients. We show on simulated and real images the denoising results. This algorithm is then used for the deconvolution purpose. |
|
10 - SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model.
A. Achim and E.E. Kuruoglu and J. Zerubia. Research Report 5493, INRIA, France, February 2005.
Keywords : Synthetic Aperture Radar (SAR), MAP estimation, Alpha-stable distribution, Mellin transform.
@TECHREPORT{5493,
|
| author |
= |
{Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model}, |
| year |
= |
{2005}, |
| month |
= |
{February}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5493}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00070514}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/70514/filename/RR-5493.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/05/14/PS/RR-5493.ps}, |
| keyword |
= |
{Synthetic Aperture Radar (SAR), MAP estimation, Alpha-stable distribution, Mellin transform} |
| } |
Résumé :
Les images issues d'un radar à synthèse d'ouverture (RSO) sont affectées de manière inhérente par un bruit dépendant du signal, généralement connu sous le nom de bruit de chatoiement et qui est dû à la cohérence de l'onde radar. Dans ce rapport, nous proposons un nouveau filtre adaptatif pour débruiter les images RSO et nous déduisons un estimateur du maximum a posteriori (MAP) pour la section efficace du diagramme de gain en radar. On utilise d'abord une transformée logarithmique afin de changer le bruit multiplicatif en bruit additif. Nous modélisons la section efficace à l'aide d'une densité de probabilité récemment introduite - la densité de Rayleigh à queue lourde, qui a été obtenue en supposant que les parties réelles et imaginaires du signal complexe reçu peuvent être mieux caractérisées à l'aide de la famille des distributions alpha-stables. Nous estimons les paramètres du modèle à partir d'observations bruitées en faisant appel à la théorie statistique de deuxième espèce qui est fondée sur la transformée de Mellin. Enfin, nous faisons la comparaison entre la méthode que nous proposons et d'autres filtres classiques pour le débruitage d'images RSO. Nos résultats expérimentaux démontrent que le filtre MAP homomorphique fondé sur le modèle de Rayleigh à queue lourde est parmi les meilleurs pour enlever le bruit de chatoiement. |
Abstract :
Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this report, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare our proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal. |
|
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Collection article or Book chapter |
1 - A Reversible Jump MCMC Sampler for Object Detection in Image Processing.
M. Ortner and X. Descombes and J. Zerubia. In Monte Carlo and Quasi-Monte Carlo Methods, Publ. Springer Verlag, 2005.
Keywords : RJMCMC, Marked point process, Object extraction.
@INCOLLECTION{ortner_lnsmc2qmc,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Reversible Jump MCMC Sampler for Object Detection in Image Processing}, |
| year |
= |
{2005}, |
| booktitle |
= |
{Monte Carlo and Quasi-Monte Carlo Methods}, |
| publisher |
= |
{Springer Verlag}, |
| url |
= |
{http://link.springer.com/book/10.1007/3-540-31186-6}, |
| pdf |
= |
{http://link.springer.com/chapter/10.1007%2F3-540-31186-6_23}, |
| keyword |
= |
{RJMCMC, Marked point process, Object extraction} |
| } |
|
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