|
|
The Publications
Result of the query in the list of publications :
101 Articles |
41 - Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques.
F. Lafarge and X. Descombes and J. Zerubia and S. Mathieu. Traitement du Signal, 24(1), 2007.
Note : copyright Traitement du Signal
Keywords : Gaussian Field, Rare event, DT-caracteristic, Intensity peak.
@ARTICLE{lafarge_ts06,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Mathieu, S.}, |
| title |
= |
{Détection de feux de forêt par analyse statistique d'évènements rares à partir d'images infrarouges thermiques}, |
| year |
= |
{2007}, |
| journal |
= |
{Traitement du Signal}, |
| volume |
= |
{24}, |
| number |
= |
{1}, |
| note |
= |
{copyright Traitement du Signal}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_ts06.pdf}, |
| keyword |
= |
{Gaussian Field, Rare event, DT-caracteristic, Intensity peak} |
| } |
|
42 - Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images.
A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. International Journal of Simulation Modelling, 6(2): pages 73--83, 2007.
@ARTICLE{Bhattacharya07,
|
| author |
= |
{Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images}, |
| year |
= |
{2007}, |
| journal |
= |
{International Journal of Simulation Modelling}, |
| volume |
= |
{6}, |
| number |
= |
{2}, |
| pages |
= |
{73--83}, |
| url |
= |
{http://www.ijsimm.com/Full_Papers/Fulltext2007/text6-2_73-83.pdf}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Bhattacharya07.pdf}, |
| keyword |
= |
{} |
| } |
Abstract :
Indexing and retrieval from remote sensing image databases relies on the extraction of appropriate information from the data about the entity of interest (e.g. land cover type) and on the robustness of this extraction to nuisance variables. Other entities in an image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. The road network contained in an image is one example. The properties of road networks vary considerably from one geographical environment to another, and they can therefore be used to classify and retrieve such environments. In this paper, we define several such environments, and classify them with the aid of geometrical and topological features computed from the road networks occurring in them. The relative failure of network extraction methods in certain types of urban area obliges us to segment such areas and to add a second set of geometrical and topological features computed from the segmentations. To validate the approach, feature selection and SVM linear kernel classification are performed on the feature set arising from a diverse image database. |
|
43 - 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 |
|
44 - 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. |
|
45 - 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. |
|
46 - 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. |
|
47 - 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. |
|
48 - 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. |
|
49 - 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. |
|
50 - 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. |
|
51 - 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 |
= |
{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. |
|
52 - 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 |
= |
{} |
| } |
|
53 - 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} |
| } |
|
54 - 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} |
| } |
|
55 - 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. |
|
56 - 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 |
= |
{} |
| } |
|
57 - 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 |
= |
{84}, |
| number |
= |
{1}, |
| pages |
= |
{15--35}, |
| pdf |
= |
{http://www.math.u-bordeaux1.fr/~jaujol/HDR/A2.pdf}, |
| keyword |
= |
{} |
| } |
|
58 - 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 |
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{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
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{Image Decomposition into a Bounded Variation Component and an Oscillating Component}, |
| year |
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{2005}, |
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{http://link.springer.com/article/10.1007/s10851-005-4783-8}, |
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{} |
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|
59 - 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 |
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{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
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{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} |
| } |
|
60 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 108--125, September 2004.
Note : in Russian
@ARTICLE{DES04br,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
= |
{Applications of Gibbs fields methods to image processing problems}, |
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= |
{2004}, |
| month |
= |
{September}, |
| journal |
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{Problems of Information Transmission}, |
| volume |
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{40}, |
| number |
= |
{3}, |
| pages |
= |
{108--125}, |
| note |
= |
{in Russian}, |
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= |
{http://www.mathnet.ru/php/getFT.phtml?jrnid=ppi&paperid=146&what=fullt&option_lang=rus}, |
| keyword |
= |
{} |
| } |
|
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