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Publications of Josiane Zerubia
Result of the query in the list of publications :
173 Conference articles |
12 - Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar.
Y. Verdié and F. Lafarge and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011.
Keywords : 3D-Modeling, shape analysis, Mesh processing.
@INPROCEEDINGS{VerdieICIP11,
|
| author |
= |
{Verdié, Y. and Lafarge, F. and Zerubia, J.}, |
| title |
= |
{Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar}, |
| year |
= |
{2011}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00605623/fr/}, |
| keyword |
= |
{3D-Modeling, shape analysis, Mesh processing} |
| } |
Abstract :
We present an automatic approach for modeling buildings from aerial LiDAR data. The method produces accurate, watertight and compact meshes under planar constraints which are especially designed for urban scenes. The LiDAR point cloud is classified through a non-convex energy minimization problem in order to separate the points labeled as building. Roof structures are then extracted from this point subset, and used to control the meshing procedure. Experiments highlight the potential of our method in term of minimal rendering, accuracy and compactness |
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13 - Morphological road segmentation in urban areas from high resolution satellite images.
R. Gaetano and J. Zerubia and G. Scarpa and G. Poggi. In International Conference on Digital Signal Processing, Corfu, Greece, July 2011.
Keywords : Segmentation, Classification, skeletonization , pattern recognition, shape analysis.
@INPROCEEDINGS{GaetanoDSP,
|
| author |
= |
{Gaetano, R. and Zerubia, J. and Scarpa, G. and Poggi, G.}, |
| title |
= |
{Morphological road segmentation in urban areas from high resolution satellite images}, |
| year |
= |
{2011}, |
| month |
= |
{July}, |
| booktitle |
= |
{International Conference on Digital Signal Processing}, |
| address |
= |
{Corfu, Greece}, |
| url |
= |
{http://hal.inria.fr/inria-00618222/fr/}, |
| keyword |
= |
{Segmentation, Classification, skeletonization , pattern recognition, shape analysis} |
| } |
Abstract :
High resolution satellite images provided by the last generation
sensors significantly increased the potential of almost
all the image information mining (IIM) applications related
to earth observation. This is especially true for the extraction
of road information, task of primary interest for many remote
sensing applications, which scope is more and more extended
to complex urban scenarios thanks to the availability of highly
detailed images. This context is particularly challenging due
to such factors as the variability of road visual appearence
and the occlusions from entities like trees, cars and shadows.
On the other hand, the peculiar geometry and morphology of
man-made structures, particularly relevant in urban areas, is
enhanced in high resolution images, making this kind of information
especially useful for road detection.
In this work, we provide a new insight on the use of morphological
image analysis for road extraction in complex urban
scenarios, and propose a technique for road segmentation
that only relies on this domain. The keypoint of the technique
is the use of skeletons as powerful descriptors for road objects:
the proposed method is based on an ad-hoc skeletonization
procedure that enhances the linear structure of road segments,
and extracts road objects by first detecting their skeletons
and then associating each of them with a region of the
image. Experimental results are presented on two different
high resolution satellite images of urban areas. |
|
14 - A novel algorithm for occlusions and perspective effects using a 3d object process.
A. Gamal Eldin and X. Descombes and J. Zerubia. In ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing), Prague, Czech Republic, May 2011.
Keywords : Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting.
@INPROCEEDINGS{ICASSP_2011,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A novel algorithm for occlusions and perspective effects using a 3d object process}, |
| year |
= |
{2011}, |
| month |
= |
{May}, |
| booktitle |
= |
{ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing)}, |
| address |
= |
{Prague, Czech Republic}, |
| url |
= |
{http://hal.inria.fr/inria-00592449/fr/}, |
| keyword |
= |
{Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting} |
| } |
Abstract :
In this paper, we introduce a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying in the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. These configurations are projected onto the image plane. The configurations are modified until convergence using the multiple birth and death optimization algorithm and by measuring the similarity between the projected image of the configuration and the real image. During optimization, the proposed configuration is modeled by a mixed graph which represents all dependencies between the objects, including interaction between neighbor objects and parent-child dependency for occluded objects. We tested our model on synthetic image, and real images. |
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15 - Wavefront sensing for aberration modeling in fluorescence MACROscopy.
P. Pankajakshan and A. Dieterlen and G. Engler and Z. Kam and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, USA, April 2011.
Keywords : fluorescence MACROscopy , phase retrieval, field aberration.
@INPROCEEDINGS{PanjakshanISBI2011,
|
| author |
= |
{Pankajakshan, P. and Dieterlen, A. and Engler, G. and Kam, Z. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{Wavefront sensing for aberration modeling in fluorescence MACROscopy}, |
| year |
= |
{2011}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| address |
= |
{Chicago, USA}, |
| url |
= |
{http://hal.inria.fr/inria-00563988/en/}, |
| keyword |
= |
{fluorescence MACROscopy , phase retrieval, field aberration} |
| } |
Abstract :
In this paper, we present an approach to calculate the wavefront in
the back pupil plane of an objective in a fluorescent MACROscope.
We use the three-dimensional image of a fluorescent bead because it
contains potential pupil information in the ‘far’ out-of-focus planes
for sensing the wavefront at the back focal plane of the objective.
Wavefront sensing by phase retrieval technique is needed for several
reasons. Firstly, the point-spread function of the imaging system
can be calculated from the estimated pupil phase and used for image
restoration. Secondly, the aberrations in the optics of the objective
can be determined by studying this phase. Finally, the estimated
wavefront can be used to correct the aberrated optical path with-
out a wavefront sensor. In this paper, we estimate the wavefront of
a MACROscope optical system by using Bayesian inferencing and
derive the Gerchberg-Saxton algorithm as a special case. |
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16 - Multiple Birth and Cut Algorithm for Point Process Optimization.
A. Gamal Eldin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS), Kuala Lumpur, Malaysia, December 2010.
Keywords : Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process.
@INPROCEEDINGS{MBC_MPP_SITIS10,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Multiple Birth and Cut Algorithm for Point Process Optimization}, |
| year |
= |
{2010}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS)}, |
| address |
= |
{Kuala Lumpur, Malaysia}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00516305/fr/}, |
| keyword |
= |
{Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process} |
| } |
Abstract :
In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to the energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing the advantages and disadvantages, where the most important advantage is the reduction of the number of parameters. We validated our algorithm on the counting problem of flamingos in colony, where our algorithm outperforms the performance of the MBD algorithm. |
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17 - A theoretical and numerical study of a phase field higher-order active contour model of directed networks.
A. El Ghoul and I. H. Jermyn and J. Zerubia. In The Tenth Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand, November 2010.
Keywords : Phase Field, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing.
Copyright : Springer-Verlag GmbH Berlin Heidelberg
@INPROCEEDINGS{Elghoul10b,
|
| author |
= |
{El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, |
| title |
= |
{A theoretical and numerical study of a phase field higher-order active contour model of directed networks}, |
| year |
= |
{2010}, |
| month |
= |
{November}, |
| booktitle |
= |
{The Tenth Asian Conference on Computer Vision (ACCV)}, |
| address |
= |
{Queenstown, New Zealand}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00522443/fr/}, |
| keyword |
= |
{Phase Field, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing} |
| } |
Abstract :
We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce
stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest.We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images. |
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18 - Point-spread function model for fluorescence MACROscopy imaging.
P. Pankajakshan and Z. Kam and A. Dieterlen and G. Engler and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Asilomar Conference on Signals, Systems and Computers, pages 1364-136, Pacific Grove, CA, USA , November 2010.
Keywords : fluorescence MACROscopy , point-spread function, pupil function, vignetting .
@INPROCEEDINGS{PanjakshanASILOMAR2010,
|
| author |
= |
{Pankajakshan, P. and Kam, Z. and Dieterlen, A. and Engler, G. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, |
| title |
= |
{Point-spread function model for fluorescence MACROscopy imaging}, |
| year |
= |
{2010}, |
| month |
= |
{November}, |
| booktitle |
= |
{Asilomar Conference on Signals, Systems and Computers}, |
| pages |
= |
{1364-136}, |
| address |
= |
{Pacific Grove, CA, USA }, |
| url |
= |
{http://hal.inria.fr/inria-00555940/}, |
| keyword |
= |
{fluorescence MACROscopy , point-spread function, pupil function, vignetting } |
| } |
Abstract :
In this paper, we model the point-spread function (PSF) of a fluorescence MACROscope with a field aberration. The MACROscope is an imaging arrangement that is designed to directly study small and large specimen preparations without physically sectioning them. However, due to the different optical components of the MACROscope, it cannot achieve the condition of lateral spatial invariance for all magnifications. For example, under low zoom settings, this field aberration becomes prominent, the PSF varies in the lateral field, and is proportional to the distance from the center of the field. On the other hand, for larger zooms, these aberrations become gradually absent. A computational approach to correct this aberration often relies on an accurate knowledge of the PSF. The PSF can be defined either theoretically using a scalar diffraction model or empirically by acquiring a three-dimensional image of a fluorescent bead that approximates a point source. The experimental PSF is difficult to obtain and can change with slight deviations from the physical conditions. In this paper, we model the PSF using the scalar diffraction approach, and the pupil function is modeled by chopping it. By comparing our modeled PSF with an experimentally obtained PSF, we validate our hypothesis that the spatial variance is caused by two limiting optical apertures brought together on different conjugate planes. |
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19 - Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images.
S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010.
Keywords : Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM).
@INPROCEEDINGS{sbenhadj10a,
|
| author |
= |
{Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, |
| address |
= |
{Paris, France}, |
| url |
= |
{http://hal.archives-ouvertes.fr/docs/00/52/63/45/PDF/ISPRS_SBH_FC_XD_JZ_Final2.pdf}, |
| keyword |
= |
{Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM)} |
| } |
|
20 - Tree crown detection in high resolution optical and LiDAR images of tropical forest.
J. Zhou and C. Proisy and X. Descombes and I. Hedhli and N. Barbier and J. Zerubia and J.-P. Gastellu-Etchegorry and P. Couteron. In Proc. SPIE Symposium on Remote Sensing, Toulouse, France, September 2010.
Keywords : Tropical forest, tree detection, Marked point process.
@INPROCEEDINGS{Zhou10,
|
| author |
= |
{Zhou, J. and Proisy, C. and Descombes, X. and Hedhli, I. and Barbier, N. and Zerubia, J. and Gastellu-Etchegorry, J.-P. and Couteron, P.}, |
| title |
= |
{Tree crown detection in high resolution optical and LiDAR images of tropical forest}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://dx.doi.org/10.1117/12.865068}, |
| keyword |
= |
{Tropical forest, tree detection, Marked point process} |
| } |
|
21 - Multi-spectral Image Analysis for Skin Pigmentation Classification.
S. Prigent and X. Descombes and D. Zugaj and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Hong-Kong, China, September 2010.
Keywords : skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction.
@INPROCEEDINGS{sp02,
|
| author |
= |
{Prigent, S. and Descombes, X. and Zugaj, D. and Martel, P. and Zerubia, J.}, |
| title |
= |
{Multi-spectral Image Analysis for Skin Pigmentation Classification}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Hong-Kong, China}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/49/94/92/PDF/Article_ICIP.pdf}, |
| keyword |
= |
{skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction} |
| } |
Abstract :
In this paper, we compare two different approaches for semi-automatic detection of skin hyper-pigmentation on multi-spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.
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