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Publications of 2006
Result of the query in the list of publications :
8 Articles |
1 - SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model.
A. Achim and E.E. Kuruoglu and J. Zerubia. IEEE Trans. on Image Processing, 15(9): pages 2686-2693, September 2006.
Keywords : SAR Images.
@ARTICLE{jz_ieee_tr_ip_06,
|
| author |
= |
{Achim, A. and Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{SAR Image Filtering Based on the Heavy-Tailed Rayleigh Model}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{15}, |
| number |
= |
{9}, |
| pages |
= |
{2686-2693}, |
| pdf |
= |
{http://dx.doi.org/10.1109/TIP.2006.877362}, |
| keyword |
= |
{SAR Images} |
| } |
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 paper, 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 the 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 |
|
2 - Higher Order Active Contours.
M. Rochery and I. H. Jermyn and J. Zerubia. International Journal of Computer Vision, 69(1): pages 27--42, August 2006.
Keywords : Active contour, Shape, Higher-order, Prior, Road network.
@ARTICLE{mr_ijcv_06,
|
| author |
= |
{Rochery, M. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Higher Order Active Contours}, |
| year |
= |
{2006}, |
| month |
= |
{August}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{69}, |
| number |
= |
{1}, |
| pages |
= |
{27--42}, |
| url |
= |
{http://dx.doi.org/10.1007/s11263-006-6851-y}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_mr_ijcv_06.pdf}, |
| keyword |
= |
{Active contour, Shape, Higher-order, Prior, Road network} |
| } |
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. |
|
3 - SAR amplitude probability density function estimation based on a generalized Gaussian model.
G. Moser and J. Zerubia and S.B. Serpico. IEEE Trans. on Image Processing, 15(6): pages 1429-1442, June 2006.
Keywords : SAR Images, Generalised Gaussians, Mellin transform.
Copyright : IEEE
@ARTICLE{moser_ieeeip05,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{SAR amplitude probability density function estimation based on a generalized Gaussian model}, |
| year |
= |
{2006}, |
| month |
= |
{June}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{15}, |
| number |
= |
{6}, |
| pages |
= |
{1429-1442}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1632197}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00561372/en/}, |
| keyword |
= |
{SAR Images, Generalised Gaussians, Mellin transform} |
| } |
Abstract :
In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed “method-of-log-cumulants” (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena. |
|
4 - Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution.
N. Dey and L. Blanc-Féraud and C. Zimmer and Z. Kam and P. Roux and J.C. Olivo-Marin and J. Zerubia. Microscopy Research Technique, 69: pages 260-266, April 2006.
Keywords : Confocal microscopy, Variational methods, Total variation, Deconvolution.
@ARTICLE{dey_mrt_05,
|
| author |
= |
{Dey, N. and Blanc-Féraud, L. and Zimmer, C. and Kam, Z. and Roux, P. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Richardson-Lucy Algorithm with Total Variation Regularization for 3D Confocal Microscope Deconvolution}, |
| year |
= |
{2006}, |
| month |
= |
{April}, |
| journal |
= |
{Microscopy Research Technique}, |
| volume |
= |
{69}, |
| pages |
= |
{260-266}, |
| url |
= |
{http://dx.doi.org/10.1002/jemt.20294}, |
| keyword |
= |
{Confocal microscopy, Variational methods, Total variation, Deconvolution} |
| } |
Abstract :
Confocal laser scanning microscopy is a powerful and popular technique for 3D imaging of biological specimens. Although confocal microscopy images are much sharper than standard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poisson noise due to photon-limited
detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes a maximum likelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regularization constraints based on some prior knowledge on the data have to be applied to stabilize the solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges. We
show on simulated and real images that this constraint improves the deconvolution results as compared to the unregularized Richardson-Lucy algorithm, both visually and quantitatively. |
|
5 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation.
H. Permuter and J.M. Francos and I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, April 2006.
Keywords : Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion.
@ARTICLE{permuter_pr06,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, |
| year |
= |
{2006}, |
| month |
= |
{April}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{39}, |
| number |
= |
{4}, |
| pages |
= |
{695--706}, |
| url |
= |
{http://dx.doi.org/10.1016/j.patcog.2005.10.028}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_permuter_pr06.pdf}, |
| keyword |
= |
{Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion} |
| } |
Abstract :
The aims of this paper are two-fold: to define Gaussian mixture models of coloured texture on several feature paces and to compare the performance of these models
in various classification tasks, both with each other and with other models popular in the literature. We construct Gaussian mixtures models over a variety of different colour and texture feature spaces, with a view to the retrieval of textured colour images from databases. We compare supervised classification results for different choices of colour and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'colour' and 'structure' information in order to improve the classification performance. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance. |
|
6 - Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation.
G. Moser and J. Zerubia and S.B. Serpico. IEEE Trans. Geoscience and Remote Sensing, 44(1): pages 188-200, January 2006.
Keywords : SAR Images, Stochastic EM (SEM), Dictionary.
Copyright : IEEE
@ARTICLE{moser_ieeetgrs_05,
|
| author |
= |
{Moser, G. and Zerubia, J. and Serpico, S.B.}, |
| title |
= |
{Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{44}, |
| number |
= |
{1}, |
| pages |
= |
{188-200}, |
| url |
= |
{http://dx.doi.org/10.1109/TGRS.2005.859349}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00561369/en/}, |
| keyword |
= |
{SAR Images, Stochastic EM (SEM), Dictionary} |
| } |
Abstract :
In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation–maximization iterative methodology with the recently developed “method-of-log-cumulants” for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data. |
|
7 - An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals.
G. Aubert and L. Blanc-Féraud and R. March. Nonlinear Analysis, 64: pages 1908-1930, 2006.
Keywords : Gamma Convergence, Finite Element, Segmentation.
@ARTICLE{laure-na05,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{An approximation of the Mumford-Shah energy by a family of dicrete edge-preserving functionals}, |
| year |
= |
{2006}, |
| journal |
= |
{Nonlinear Analysis}, |
| volume |
= |
{64}, |
| pages |
= |
{1908-1930}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_laure-na05.pdf}, |
| keyword |
= |
{Gamma Convergence, Finite Element, Segmentation} |
| } |
Abstract :
We show the Gamma-convergence of a family of discrete functionals to the Mumford and Shah image segmentation functional.
The functionals of the family are constructed by modifying the elliptic approximating functionals proposed by Ambrosio and Tortorelli. The quadratic term of the energy related to the edges of the segmentation is replaced by a nonconvex functional. |
|
8 - Automatic building 3D reconstruction from DEMs.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 184: pages 48--53, 2006.
Keywords : 3D-reconstruction, Digital Elevation Model, Building extraction, dense urban areas.
@ARTICLE{lafarge_sfpt06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic building 3D reconstruction from DEMs}, |
| year |
= |
{2006}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| volume |
= |
{184}, |
| pages |
= |
{48--53}, |
| url |
= |
{http://isprs.free.fr/documents/Papers/T07-32.pdf}, |
| keyword |
= |
{3D-reconstruction, Digital Elevation Model, Building extraction, dense urban areas} |
| } |
Abstract :
This paper is about an example of PLEIADES applications, the 3D building reconstruction. The future PLEIADES satellites are
especially well adapted to deal with 3D building reconstruction through the sub-metric resolution of images and its stereoscopic characteristics. We propose a fully automatic 3D-city model of dense urban areas using a parametric approach. First, a Digital Elevation
Model (DEM) is generated using an algorithm based on a maximum-flow formulation using three views. Then, building footprints are extracted from the DEM through an automatic method based on marked point processes : they are represented by an association of rectangles that we regularize by improving the connection of the neighboring rectangles and the facade discontinuity detection. Finally, a 3D-reconstruction method based on a skeleton process which allows to model the rooftops is proposed from the DEM and the building footprints. The different building heights constitute parameters which are estimated and then regularized by the ”K-means” algorithm including an entropy term. |
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PhD Thesis and Habilitation |
1 - Etude du couvert forestier par processus ponctuels marqués.
G. Perrin. PhD Thesis, Ecole Centrale Paris, October 2006.
Keywords : Tree Crown Extraction, Marked point process, Stochastic geometry, Object extraction, RJMCMC.
@PHDTHESIS{perrin_phd06,
|
| author |
= |
{Perrin, G.}, |
| title |
= |
{Etude du couvert forestier par processus ponctuels marqués}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| school |
= |
{Ecole Centrale Paris}, |
| url |
= |
{http://www-sop.inria.fr/ariana/personnel/Guillaume.Perrin/resume.php}, |
| pdf |
= |
{http://www-sop.inria.fr/ariana/personnel/Guillaume.Perrin/DOWNLOADS/these_perrin_2006.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Marked point process, Stochastic geometry, Object extraction, RJMCMC} |
| } |
Résumé :
Cette thèse aborde le problème de l'extraction d'arbres à partir d'images aériennes InfraRouge Couleur (IRC) de forêts. Nos modèles reposent sur l'utilisation de processus objets ou processus ponctuels marqués. Il s'agit de variables aléatoires dont les réalisations sont des configurations d'objets géométriques. Une fois l'objet géométrique de référence choisi, nous définissons l'énergie du processus par le biais d'un terme a priori, modélisant les contraintes sur les objets et leurs interactions, ainsi qu'un terme image. Nous échantillonnons le processus objet grâce à un algorithme de type Monte Carlo par Chaînes de Markov à sauts réversibles (RJMCMC), optimisé par un recuit simulé afin d'extraire la meilleure configuration d'objets, qui nous donne l'extraction recherchée.
Dans ce manuscrit, nous proposons différents modèles d'extraction de houppiers, qui extraient des informations à l'échelle de l'arbre selon la densité du peuplement. Dans les peuplements denses, nous présentons un processus d'ellipses, et dans les zones de plus faible densité, un processus d'ellipsoïdes. Nous obtenons ainsi le nombre d'arbres, leur localisation, le diamètre de la couronne et leur hauteur pour les zones non denses. Les algorithmes automatiques résultant de cette modélisation sont testés sur des images IRC très haute résolution fournies par l'Inventaire Forestier National (IFN). |
Abstract :
This thesis addresses the problem of tree crown extraction from Colour InfraRed (CIR) aerial images of forests. Our models are based on object processes, otherwise known as marked point processes. These mathematical objects are random variables whose realizations are configurations of geometrical shapes. This approach yields an energy minimization problem, where the energy is composed of a regularization term (prior density), which introduces some constraints on the objects and their interactions, and a data term, which links the objects to the features to be extracted. Once the reference object has been chosen, we sample the process and extract the best configuration of objects with respect to the energy, using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm embedded in a Simulated Annealing scheme.
We propose different models for tree crown extraction depending on the density of the stand. In dense areas, we use an ellipse process, while in sparse vegetation an ellipsoïd process is used. As a result we obtain the number of stems, their position, the diameters of the crowns and the heights of the trees for sparse areas. The resulting algorithms are tested on high resolution CIR aerial images provided by the French National Forest Inventory (IFN). |
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top of the page
15 Conference articles |
1 - An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP), Madurai, India, December 2006.
Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape.
@INPROCEEDINGS{Horvath06_icvgip,
|
| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{An improved 'gas of circles' higher-order active contour model and its application to tree crown extraction}, |
| year |
= |
{2006}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. Indian Conference on Computer Vision, Graphics, and Image Processing (ICVGIP)}, |
| address |
= |
{Madurai, India}, |
| url |
= |
{http://dx.doi.org/10.1007/11949619_14}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath06_icvgip.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} |
| } |
Abstract :
A central task in image processing is to find the
region in the image corresponding to an entity. In a
number of problems, the region takes the form of a
collection of circles, eg tree crowns in remote
sensing imagery; cells in biological and medical
imagery. In~citeHorvath06b, a model of such regions,
the `gas of circles' model, was developed based on
higher-order active contours, a recently developed
framework for the inclusion of prior knowledge in
active contour energies. However, the model suffers
from a defect. In~citeHorvath06b, the model
parameters were adjusted so that the circles were local
energy minima. Gradient descent can become stuck in
these minima, producing phantom circles even with no
supporting data. We solve this problem by calculating,
via a Taylor expansion of the energy, parameter values
that make circles into energy inflection points rather
than minima. As a bonus, the constraint halves the
number of model parameters, and severely constrains one
of the two that remain, a major advantage for an
energy-based model. We use the model for tree crown
extraction from aerial images. Experiments show that
despite the lack of parametric freedom, the new model
performs better than the old, and much better than a
classical active contour. |
|
2 - Burnt area mapping using Support Vector Machines.
O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Forest Fire Research, Figueira da Foz, Portugal, November 2006.
Keywords : Satellite images, Forest fires, Burnt areas, Support Vector Machines.
@INPROCEEDINGS{zammit_icffr_06,
|
| author |
= |
{Zammit, O. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Burnt area mapping using Support Vector Machines}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. International Conference on Forest Fire Research}, |
| address |
= |
{Figueira da Foz, Portugal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_zammit_icffr_06.pdf}, |
| keyword |
= |
{Satellite images, Forest fires, Burnt areas, Support Vector Machines} |
| } |
|
3 - An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), Atlanta, October 2006.
Keywords : 3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images.
Copyright : IEEE
@INPROCEEDINGS{lafarge_icip06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic Building Reconstruction Method : A Structural Approach Using High Resolution Images}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Atlanta}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_icip06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, RJMCMC, Structural approach, Satellite images} |
| } |
|
4 - Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval.
A. Bhattacharya and I. H. Jermyn and X. Descombes and J. Zerubia. In Proc. compImage, Coimbra, Portugal, October 2006.
Keywords : Road network, Indexation, Semantic, Retrieval, Feature statistics.
@INPROCEEDINGS{bhatta_compimage06,
|
| author |
= |
{Bhattacharya, A. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval}, |
| year |
= |
{2006}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. compImage}, |
| address |
= |
{Coimbra, Portugal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_bhatta_compimage06.pdf}, |
| keyword |
= |
{Road network, Indexation, Semantic, Retrieval, Feature statistics} |
| } |
Abstract :
Retrieval from remote sensing image archives relies on the
extraction of pertinent information from the data about the entity of interest (e.g. land cover type), and on the robustness of this extraction to nuisance variables (e.g. illumination). Most image-based characterizations are not invariant to such variables. However, other semantic entities in the image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. Road networks are one example: their properties vary considerably, for example, from urban to rural areas. This paper takes the first steps towards classification (and hence retrieval) based on this idea. We study the dependence of a number of network features on the class of the image ('urban' or 'rural'). The chosen features include measures of the network density, connectedness, and `curviness'. The feature distributions of the two classes are well separated in feature space, thus providing a basis for retrieval. Classification using kernel k-means confirms this conclusion. |
|
5 - Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture.
J. Aubray and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Florence, Italy, September 2006.
Keywords : Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics.
@INPROCEEDINGS{aubray_eusipco06,
|
| author |
= |
{Aubray, J. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{Nonlinear models for the statistics of adaptive wavelet packet coefficients of texture}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Florence, Italy}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_aubray_eusipco06.pdf}, |
| keyword |
= |
{Texture, Adaptive, Wavelet packet, Nonlinear, Bimodal, Statistics} |
| } |
Abstract :
Probabilistic adaptive wavelet packet models of
texture pro- vide new insight into texture structure
and statistics by focus- ing the analysis on
significant structure in frequency space. In very
adapted subbands, they have revealed new bimodal
statistics, corresponding to the structure inherent to
a texture, and strong dependencies between such
bimodal sub- bands, related to phase coherence in a
texture. Existing models can capture the former but
not the latter. As a first step to- wards modelling
the joint statistics, and in order to simplify earlier
approaches, we introduce a new parametric family of
models capable of modelling both bimodal and unimodal
subbands, and of being generalized to capture the
joint statistics. We show how to compute MAP estimates
for the adaptive basis and model parameters, and apply
the models to Brodatz textures to illustrate their
performance. |
|
6 - 2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong-Kong, August 2006.
Keywords : Data energy, Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process.
@INPROCEEDINGS{perrin_06_c,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{2D and 3D Vegetation Resource Parameters Assessment using Marked Point Processes}, |
| year |
= |
{2006}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Hong-Kong}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_perrin_06_c.pdf}, |
| keyword |
= |
{Data energy, Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process} |
| } |
Abstract :
High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the forest resources, from which can be deduced information of biodiversity and ecological sustainability. In that prospect, automatic algorithms are needed to give a further exploitation of the data and to assist human operators. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the positions of the trees and marks their geometric features. This approach gives also the number of stems, their position, and their size. It is an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Results are shown on aerial images provided by the French National Forest Inventory (IFN). |
|
7 - A Higher-Order Active Contour Model for Tree Detection.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Hong Kong, August 2006.
Keywords : Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction.
@INPROCEEDINGS{horvath_icpr06,
|
| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{A Higher-Order Active Contour Model for Tree Detection}, |
| year |
= |
{2006}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Hong Kong}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_horvath_icpr06.pdf}, |
| keyword |
= |
{Active contour, Gas of circles, Higher-order, Shape, Prior, Tree Crown Extraction} |
| } |
Abstract :
We present a model of a ‘gas of circles’, the ensemble
of regions in the image domain consisting of an
unknown number of circles with approximately fixed
radius and short range repulsive interactions, and
apply it to the extraction of tree crowns from aerial
images. The method uses the re- cently introduced
‘higher order active contours’ (HOACs), which
incorporate long-range interactions between contour
points, and thereby include prior geometric
information without using a template shape. This makes
them ideal when looking for multiple instances of an
entity in an image. We study an existing HOAC model
for networks, and show via a stability calculation
that circles stable to perturbations are possible
for constrained parameter sets. Combining this prior
energy with a data term, we show results on aerial
imagery that demonstrate the effectiveness of the
method and the need for prior geometric knowledge. The
model has many other potential applications. |
|
8 - Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS), Marne La Vallee, France, July 2006.
Keywords : 3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations.
@INPROCEEDINGS{lafarge_isprs06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic 3D Building Reconstruction from DEMs: an Application to PLEIADES Simulations}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing Commission I Symposium (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_isprs06.pdf}, |
| keyword |
= |
{3D reconstruction, Digital Elevation Model, Building extraction, Dense urban areas, PLEIADES simulations} |
| } |
|
9 - A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests.
M. Eriksson and G. Perrin and X. Descombes and J. Zerubia. In Proc. International Society for Photogrammetry and Remote Sensing (ISPRS), Marne La Vallee, France, July 2006.
Keywords : Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction.
@INPROCEEDINGS{eriksson06a,
|
| author |
= |
{Eriksson, M. and Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A comparative study of three methods for identifying individual tree crowns in aerial images covering different types of forests}, |
| year |
= |
{2006}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. International Society for Photogrammetry and Remote Sensing (ISPRS)}, |
| address |
= |
{Marne La Vallee, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_eriksson06a.pdf}, |
| keyword |
= |
{Region Growing, Marked point process, Markov Fields, Object extraction, Tree Crown Extraction} |
| } |
Abstract :
Most of today's silviculture methods has the goal to optimise the outcome of the forest in stem volume when it is cut. It might also be relevant to save parts of the forest, for instance, to protect a habitat. In order to get a good survey of the forest, remote sensed images are often used. These images are most often manually interpreted in combination with field measurements in order to estimate the forest parameters that are of importance in the decision how to optimally maintain the forest. Among these parameters the most common are stem number, stem volume, and tree species. Interpretation of images are often labour and time consuming. Thus, automatically developed methods for interpretation can lower the work load and speed up the interpretation time.
The interpretation is often done using images captured from a far distance from the ground in order to capture as large area as possible. However, this lower the accuracy of the estimates since it must be done stand wise. Knowledge of where each individual trees in the forest is located together with its size will increase accuracy. It makes it also possible to plan the cutting in detail. With this knowledge in mind, research about finding automatically methods for finding individual tree crowns in aerial images has been a subject for researchers the last decades.
Today's methods are not capable to alone handle all kind of forests. Therefore, comparative studies of different segmentation methods with different types of forests are of importance in order to clarify how much a method is reliable at a certain type of forest. This knowledge can, for instance, be used to build up an expert system which are supposed to be able to find individual tree crowns in any kind of forests. The comparison is done using images covering different types of forests. The types of forests that are included in the study ranges from isolated tree crown where the ground is clearly visible between the crowns to dense forest which is naturally regenerated via planted forest.
In this study we compare three existing segmentation methods for extracting individual tree crowns from aerial images. The first two methods are probabilistic methods which minimises some energy function while the third is a region growing algorithm. The first probabilistic method is based on a Markov Random Field modelling. We define a prior Markov model to segment the image into three classes (background, vegetation and tree centres). The prior model embed a circular shape model of the tree crown with a random radius. The data term allows to well position the tree centres onto the image and to describe the tree shape as fluctuations around the circular template. Besides, some long range interactions models the relations between the trees locations, such as some periodicity in case of plantations.
The second probabilistic method consists in modeling the trees in the forestry images as random configurations of ellipses or ellipsoids, whose points are the positions of the stems and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. We estimate the best configuration of an unknown number of objects, from which 2D and 3D vegetation resource parameters can be extracted. To sample this marked point process, we use Monte Carlo dynamics, while the optimization is performed via a Simulated Annealing algorithm, which results in a fully automatic approach. This approach works well on plantations, where there are high spatial relations between the trees, and on isolated trees where 3D parameters can be extracted, but some difficulties remain in dense areas.
The third method, the region growing algorithm, relies as all region growing methods on good seed points, i.e. in this case approximate locations of the tree crowns. From the seed points the segments are grown according to a grey level value of the neighbouring pixels. The larger the value is the sooner it is connected to the neighbouring segment. The segments stops to grow when all pixels belongs to a segment. This method, contrary the others, will have as a result, segments that have captured the actual shape of the tree crown if the forest is not too sparse. If the forest is too sparse such that the ground is visible, there are problems of finding the seed points. In the cases when the forest is sparse, there are difficulties to separate the tree crowns from the ground. Even if the seed points would be located only at the tree crowns the result will contain a lot of errors since all pixels most belong to a segment, i.e. even the ground pixels must be connected to a segment in this case. |
|
10 - An Automatic 3D City Model : a Bayesian Approach using Satellite Images.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toulouse, France, May 2006.
Note : Copyright IEEE
Keywords : 3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM).
@INPROCEEDINGS{florenticassp06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An Automatic 3D City Model : a Bayesian Approach using Satellite Images}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Toulouse, France}, |
| note |
= |
{Copyright IEEE}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_florenticassp06.pdf}, |
| keyword |
= |
{3D reconstruction, Buildings, MCMC, Digital Elevation Model (DEM)} |
| } |
|
11 - Forest Resource Assessment using Stochastic Geometry.
G. Perrin and X. Descombes and J. Zerubia and J.G. Boureau. In Proc. International Precision Forestry Symposium, March 2006.
Keywords : Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy.
@INPROCEEDINGS{perrin_06_b,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J. and Boureau, J.G.}, |
| title |
= |
{Forest Resource Assessment using Stochastic Geometry}, |
| year |
= |
{2006}, |
| month |
= |
{March}, |
| booktitle |
= |
{Proc. International Precision Forestry Symposium}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_ipfs06.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Object extraction, Stochastic geometry, RJMCMC, Data energy} |
| } |
Abstract :
Aerial and satellite imagery has a key role to play in natural resource management, especially in forestry application. The submetric resolution of the data enables to study forests at the scale of trees, and to get a more accurate assessment of the resources such as the number of stems or the forest cover. To develop automatic tools in order to help the inventories in their work and to bring more knowledge about the stands is also nowadays of important economical and environmental concerns.
In this paper, we aim at extracting tree crowns from high resolution aerial Color Infrared images (CIR) of forests using marked point processes. Our approach consists in modelling the trees in the forestry images as random configurations of ellipses, whose points are the positions of the stems and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Our goal is to find the best configuration of an unknown number of objects, i.e. the configuration that maximizes this density. To sample this marked point process, we use Monte Carlo dynamics while the optimization is performed via a Simulated Annealing algorithm, which results in a fully automatic approach.
We present different models for the data term in order to cope with different kinds of stands : plantations, isolated trees and mixed stands. Results are shown on aerial CIR images provided by the French Forest Inventory (IFN) |
|
12 - A study of Gaussian approximations of fluorescence microscopy PSF models.
B. Zhang and J. Zerubia and J.C. Olivo-Marin. In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press, Vol. 6090, San Jose, USA, January 2006.
Copyright : SPIE
@INPROCEEDINGS{zerubia_spie06,
|
| author |
= |
{Zhang, B. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{A study of Gaussian approximations of fluorescence microscopy PSF models}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| booktitle |
= |
{Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XIII of Proc. SPIE, in press}, |
| volume |
= |
{6090}, |
| address |
= |
{San Jose, USA}, |
| keyword |
= |
{} |
| } |
|
13 - Evaluation des Ressources Forestières à l'aide de Processus Ponctuels Marqués.
G. Perrin and X. Descombes and J. Zerubia. In Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA), Tours, France, January 2006.
Keywords : Tree Crown Extraction, Stochastic geometry, Marked point process, Object extraction.
@INPROCEEDINGS{perrin_06_a,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Evaluation des Ressources Forestières à l'aide de Processus Ponctuels Marqués}, |
| year |
= |
{2006}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, |
| address |
= |
{Tours, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/perrin_rfia06.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Stochastic geometry, Marked point process, Object extraction} |
| } |
Résumé :
Les images aériennes et satellitaires jouent un role de plus en plus important dans le domaine de la gestion des ressources naturelles, et en particulier des forêts. Les organismes chargés d'en faire l'inventaire, comme l'Inventaire Forestier National (IFN) en France, s'appuient en effet sur ces images pour observer les différentes espèces d'arbres d'une zone boisée, avant de se rendre sur le terrain pour une étude plus poussée. La résolution submétrique des données permet, en outre, d'entrevoir une étude plus fine, à savoir un comptage à l'arbre près et une classification automatique des houppiers (ensemble des branches et du feuillage d'un arbre). Cette évaluation précise des ressources forestières n'est actuellement pas disponible. Aussi, le développement d'outils automatiques, chargés d'aider les gestionnaires du paysage dans leur travail en leur apportant une connaissance des ressources à l'échelle de l'arbre, se révèle-t-il être d'un intérêt grandissant.L'objectif de notre travail est donc d'extraire des houppiers à partir d'images aériennes de forêts à très haute résolution. Notre approche consiste à modéliser les peuplements forestiers par un processus ponctuel marqué d'ellipses, dont les points représentent les positions des arbres et les marques leurs caractéristiques géométriques. La densité de ce processus comporte une composante de régularisation, dite a priori, qui introduit des interactions entre les objets du processus, ainsi qu'une composante d'attache aux données, afin que les objets du processus se positionnent sur les houppiers que l'on souhaite extraire. Il s'agit de trouver la configuration d'objets, en nombre inconnu a priori, qui maximise cette densité. La simulation de tels processus fait appel aux algorithmes de type Monte Carlo par Chaîne de Markov (MCMC) à sauts réversibles, l'optimisation étant réalisée à l'aide d'un recuit simulé.Nous présentons ici un nouveau modèle d'attache aux données. Contrairement à nos précédents modèles testés sur des plantations, ce modèle n'est plus bayésien puisque le terme d'attache aux données est désormais calculé au niveau des objets et non de l'image. Ceci nous permet de travailler sur des images plus générales, avec des densités d'arbres plus variables. Des résultats obtenus sur des images fournies par l'IFN valident ce modèle. |
Abstract :
Aerial and satellite imagery has a key role to play in natural resources management, especially in forestry application. Indeed, forest inventories, such as the French National Inventory (IFN), refer to these images to analyse the different tree species in a stand, before sending a team on the ground to obtain some more advanced knowledge. Moreover, the submetric resolution of the data enables to study forests at the scale of trees, and also to get a more accurate evaluation of the resources such as the number of stems. It would be also of important economical and environmental concerns to develop automatic tools to analyze and monitor forests.We aim at extracting tree crowns from high resolution aerial images of forests. Our approach consists in modelling the forestry images as realizations of a marked point process of ellipses, whose points are the positions of the trees and marks their geometric features. The density of this process embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. Our goal is to find the best configuration of an unknown number of objects, i.e. the configuration that maximizes this density. To sample the marked point process, we use Monte Carlo dynamics (Reversible Jump Markov Chain Monte Carlo), while the optimization is performed via a simulated annealing algorithm.We present here a new model for the data term. Contrary to our previous models tested on plantations images, this model is not Bayesian anymore : the data term is calculated for each object and not for the whole image. This enables us to work on more general images, with variable tree crown densities. Example results are shown on aerial images provided by the French Forest Inventory (IFN). |
|
14 - Galaxy filament detection using the Quality candy model.
P. Gernez and X. Descombes and J. Zerubia and E. Slezak and A. Bijaoui. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006.
Keywords : Marked point process, Quality Candy model, Galaxy Filaments.
@INPROCEEDINGS{gernez06,
|
| author |
= |
{Gernez, P. and Descombes, X. and Zerubia, J. and Slezak, E. and Bijaoui, A.}, |
| title |
= |
{Galaxy filament detection using the Quality candy model}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_gernez06.pdf}, |
| keyword |
= |
{Marked point process, Quality Candy model, Galaxy Filaments} |
| } |
|
15 - Point process of segments and rectangles for building extraction from DEM.
M. Ortner and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2006.
Keywords : Stochastic geometry, Buildings.
@INPROCEEDINGS{ortner06,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point process of segments and rectangles for building extraction from DEM}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_ortner06.pdf}, |
| keyword |
= |
{Stochastic geometry, Buildings} |
| } |
|
top of the page
7 Technical and Research Reports |
1 - Hierarchical finite-state modeling for texture segmentation with application to forest classification.
G. Scarpa and M. Haindl and J. Zerubia. Research Report 6066, INRIA, INRIA, France, December 2006.
Keywords : Texture, Segmentation, Co-occurrence matrix, Structural approach, MCMC, Synthesis.
@TECHREPORT{scarparr06,
|
| author |
= |
{Scarpa, G. and Haindl, M. and Zerubia, J.}, |
| title |
= |
{Hierarchical finite-state modeling for texture segmentation with application to forest classification}, |
| year |
= |
{2006}, |
| month |
= |
{December}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6066}, |
| address |
= |
{INRIA, France}, |
| url |
= |
{https://hal.inria.fr/inria-00118420}, |
| keyword |
= |
{Texture, Segmentation, Co-occurrence matrix, Structural approach, MCMC, Synthesis} |
| } |
Abstract :
In this research report we present a new model for texture representation which is particularly well suited for image analysis and segmentation. Any image is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the Texture Fragmentation and Reconstruction (TFR) algorithm. The TFR algorithm allows to model both intra- and inter-texture interactions, and eventually addresses the segmentation task in a completely unsupervised manner. Moreover, it provides a hierarchical output, as the user may decide the scale at which the segmentation has to be given. Tests were carried out on both natural texture mosaics provided by the Prague Texture Segmentation Datagenerator Benchmark and remote-sensing data of forest areas provided by the French National Forest Inventory (IFN). |
|
2 - A higher-order active contour model of a `gas of circles' and its application to tree crown extraction.
P. Horvath and I. H. Jermyn and Z. Kato and J. Zerubia. Research Report 6026, INRIA, France, November 2006.
Keywords : Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape.
@TECHREPORT{Horvath05,
|
| author |
= |
{Horvath, P. and Jermyn, I. H. and Kato, Z. and Zerubia, J.}, |
| title |
= |
{A higher-order active contour model of a `gas of circles' and its application to tree crown extraction}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6026}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00115631}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Horvath05.pdf}, |
| keyword |
= |
{Tree Crown Extraction, Aerial images, Higher-order, Active contour, Gas of circles, Shape} |
| } |
Abstract :
Many image processing problems involve identifying the region in the image domain occupied by a given entity in the scene. Automatic solution of these problems requires models that incorporate significant prior knowledge about the shape of the region. Many methods for including such knowledge run into difficulties when the topology of the region is unknown a priori, for example when the entity is composed of an unknown number of similar objects. Higher-order active contours (HOACs) represent one method for the modelling of non-trivial prior knowledge about shape without necessarily constraining region topology, via the inclusion of non-local interactions between region boundary points in the energy defining the model. The case of an unknown number of circular objects arises in a number of domains, \eg medical, biological, nanotechnological, and remote sensing imagery. Regions composed of an a priori unknown number of circles may be referred to as a `gas of circles'. In this report, we present a HOAC model of a `gas of circles'. In order to guarantee stable circles, we conduct a stability analysis via a functional Taylor expansion of the HOAC energy around a circular shape. This analysis fixes one of the model parameters in terms of the others and constrains the rest. In conjunction with a suitable likelihood energy, we apply the model to the extraction of tree crowns from aerial imagery, and show that the new model outperforms other techniques. |
|
3 - A structural approach for 3D building reconstruction.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 6048, INRIA, November 2006.
Keywords : 3D reconstruction, Structural approach, Building, RJMCMC, Viterbi.
@TECHREPORT{Lafarge_rr_6048,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A structural approach for 3D building reconstruction}, |
| year |
= |
{2006}, |
| month |
= |
{November}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6048}, |
| url |
= |
{https://hal.inria.fr/inria-00114338}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Lafarge_rr_6048.pdf}, |
| keyword |
= |
{3D reconstruction, Structural approach, Building, RJMCMC, Viterbi} |
| } |
|
4 - Tree Crown Extraction using a Three States Markov Random Field.
X. Descombes and E. Pechersky. Research Report 5982, INRIA, September 2006.
Keywords : Markov Fields, Tree Crown Extraction.
@TECHREPORT{Descombes-Pechersky,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Tree Crown Extraction using a Three States Markov Random Field}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5982}, |
| url |
= |
{https://hal.inria.fr/inria-00097555}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Descombes-Pechersky.pdf}, |
| keyword |
= |
{Markov Fields, Tree Crown Extraction} |
| } |
|
5 - Some applications of L infinite norms in image processing.
P. Weiss and G. Aubert and L. Blanc-Féraud. Research Report 6115, INRIA, September 2006.
Keywords : projected subgradient descent, convergence rate, Total variation, compression bounded noise, meyer G norm, fast l1 minimization.
@TECHREPORT{Some applications of L infinite constraints,
|
| author |
= |
{Weiss, P. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Some applications of L infinite norms in image processing}, |
| year |
= |
{2006}, |
| month |
= |
{September}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{6115}, |
| url |
= |
{http://www.math.univ-toulouse.fr/~weiss/Publis/RR-6115.pdf}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_Some applications of L infinite constraints.pdf}, |
| keyword |
= |
{projected subgradient descent, convergence rate, Total variation, compression bounded noise, meyer G norm, fast l1 minimization} |
| } |
|
6 - An automatic building extraction method : Application to the 3D-city modeling.
F. Lafarge and P. Trontin and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5925, INRIA, France, May 2006.
Keywords : Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM).
@TECHREPORT{lafarge_rr_may06,
|
| author |
= |
{Lafarge, F. and Trontin, P. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{An automatic building extraction method : Application to the 3D-city modeling}, |
| year |
= |
{2006}, |
| month |
= |
{May}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5925}, |
| address |
= |
{France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_lafarge_rr_may06.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, 3D reconstruction, Urban areas, Satellite images, Digital Elevation Model (DEM)} |
| } |
|
7 - A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes.
G. Perrin and X. Descombes and J. Zerubia. Research Report 5846, INRIA, France, February 2006.
Keywords : Data energy, Object extraction, Tree Crown Extraction, Marked point process, Stochastic geometry, 3D reconstruction.
@TECHREPORT{rr_perrin_nonbay_05,
|
| author |
= |
{Perrin, G. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes}, |
| year |
= |
{2006}, |
| month |
= |
{February}, |
| institution |
= |
{INRIA}, |
| type |
= |
{Research Report}, |
| number |
= |
{5846}, |
| address |
= |
{France}, |
| url |
= |
{http://hal.inria.fr/inria-00070180/fr/}, |
| pdf |
= |
{http://hal.inria.fr/inria-00070180/fr/}, |
| keyword |
= |
{Data energy, Object extraction, Tree Crown Extraction, Marked point process, Stochastic geometry, 3D reconstruction} |
| } |
Résumé :
Dans ce rapport de recherche, notre but est d'extraire les houppiers à partir d'images aériennes de forêts à l'aide de processus ponctuels marqués d'ellipses ou d'ellipsoïdes. 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 la meilleure configuration d'objets, qui nous donne l'extraction recherchée.
Nous obtenons ainsi le nombre des arbres, leur localisation et leur taille. Nous présentons, dans ce rapport, un modèle 2D et un modèle 3D pour extraire des statistiques forestières. Ceux-ci sont testés sur des images aériennes infrarouge couleur très haute résolution fournies par l'Inventaire Forestier National (IFN). |
Abstract :
High resolution aerial and satellite images of forests have a key role to play in natural resource management. As they enable forestry managers to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the resources. Automatic algorithms are needed in that prospect to assist human operators in the exploitation of these data. In this paper, we present a stochastic geometry approach to extract 2D and 3D parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the locations of the trees and marks their geometric features. As a result we obtain the number of stems, their position, and their size. This approach yields an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted, in 2D and 3D. Results are shown on Colour Infrared aerial images provided by the French National Forest Inventory (IFN) |
|
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Collection article or Book chapter |
1 - Wulff Shapes at Zero Temperature for Some Models Used in Image Processing.
X. Descombes and E. Pechersky. In Statistics and Analysis of Shapes, pages 281-302, Ed. H. Krim and A. Yezzi, Publ. Birkhauser, 2006.
@INCOLLECTION{DesPechBook,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Wulff Shapes at Zero Temperature for Some Models Used in Image Processing}, |
| year |
= |
{2006}, |
| booktitle |
= |
{Statistics and Analysis of Shapes}, |
| pages |
= |
{281-302}, |
| editor |
= |
{H. Krim and A. Yezzi}, |
| publisher |
= |
{Birkhauser}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_DesPechBook.pdf}, |
| keyword |
= |
{} |
| } |
|
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