Publications of Josiane Zerubia

Result of the query in the list of publications :

• 59 Articles
• 173 Conference articles
• 64 Technical and Research Reports
• 7 Collection articles or Books chapters
• 3 Books

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59 Articles

51 - A variational model for image classification and restoration. C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. IEEE Trans. Pattern Analysis ans Machine Intelligence, 22(5): pages 460-472, May 2000. @ARTICLE{cs00, author = {Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, title = {A variational model for image classification and restoration}, year = {2000}, month = {May}, journal = {IEEE Trans. Pattern Analysis ans Machine Intelligence}, volume = {22}, number = {5}, pages = {460-472}, pdf = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=857003}, keyword = {} }

52 - A Level Set Model for Image Classification. C. Samson and L. Blanc-Féraud and G. Aubert and J. Zerubia. International Journal of Computer Vision, 40(3): pages 187-198, 2000. @ARTICLE{cs00b, author = {Samson, C. and Blanc-Féraud, L. and Aubert, G. and Zerubia, J.}, title = {A Level Set Model for Image Classification}, year = {2000}, journal = {International Journal of Computer Vision}, volume = {40}, number = {3}, pages = {187-198}, url = {http://link.springer.com/article/10.1023%2FA%3A1008183109594}, keyword = {} }

53 - Mise en correspondance et recalage de graphes~: application aux réseaux routiers extraits d'un couple carte/image. C. Hivernat and X. Descombes and S. Randriamasy and J. Zerubia. Traitement du Signal, 17(1): pages 21-32, 2000. @ARTICLE{xd00, author = {Hivernat, C. and Descombes, X. and Randriamasy, S. and Zerubia, J.}, title = {Mise en correspondance et recalage de graphes~: application aux réseaux routiers extraits d'un couple carte/image}, year = {2000}, journal = {Traitement du Signal}, volume = {17}, number = {1}, pages = {21-32}, url = {http://documents.irevues.inist.fr/handle/2042/2129}, keyword = {} }

54 - Texture analysis through a Markovian modelling and fuzzy classification: Application to urban area Extraction from Satellite Images. A. Lorette and X. Descombes and J. Zerubia. International Journal of Computer Vision, 36(3): pages 221-236, 2000. @ARTICLE{xd00a, author = {Lorette, A. and Descombes, X. and Zerubia, J.}, title = {Texture analysis through a Markovian modelling and fuzzy classification: Application to urban area Extraction from Satellite Images}, year = {2000}, journal = {International Journal of Computer Vision}, volume = {36}, number = {3}, pages = {221-236}, url = {http://dx.doi.org/10.1023/A:1008129103384}, pdf = {http://dx.doi.org/10.1023/A:1008129103384}, keyword = {} }

55 - Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood. X. Descombes and R. Morris and J. Zerubia and M. Berthod. IEEE Trans. Image Processing, 8(7): pages 954-963, July 1999. Keywords : Markov processes, Monte Carlo methods, Potts model, Image segmentation, Maximum likelihood estimation . @ARTICLE{xd99c, author = {Descombes, X. and Morris, R. and Zerubia, J. and Berthod, M.}, title = {Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood}, year = {1999}, month = {July}, journal = {IEEE Trans. Image Processing}, volume = {8}, number = {7}, pages = {954-963}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=16772&arnumber=772239&count=14&index=6}, keyword = {Markov processes, Monte Carlo methods, Potts model, Image segmentation, Maximum likelihood estimation } } Abstract : Developments in statistics now allow maximum likelihood estimators for the parameters of Markov random fields (MRFs) to be constructed. We detail the theory required, and present an algorithm that is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models-the standard Potts model, an inhomogeneous variation of the Potts model, and a long-range interaction model, better adapted to modeling real-world images. We estimate the parameters from a synthetic and a real image, and then resynthesize the models to demonstrate which features of the image have been captured by the model. Segmentations are computed based on the estimated parameters and conclusions drawn.

56 - Unsupervised parallel image classification using Markovian models. Z. Kato and J. Zerubia and M. Berthod. Pattern Recognition, 32(4): pages 591-604, 1999. Keywords : Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification. @ARTICLE{jz99a, author = {Kato, Z. and Zerubia, J. and Berthod, M.}, title = {Unsupervised parallel image classification using Markovian models}, year = {1999}, journal = {Pattern Recognition}, volume = {32}, number = {4}, pages = {591-604}, pdf = {http://dx.doi.org/10.1016/S0031-3203(98)00104-6}, keyword = {Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification} } Abstract : This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation–maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images.

57 - Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm. A. Strandlie and J. Zerubia. Computer Physics Communications, 123(1-3): pages 77-87, 1999. @ARTICLE{jz99b, author = {Strandlie, A. and Zerubia, J.}, title = {Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm}, year = {1999}, journal = {Computer Physics Communications}, volume = {123}, number = {1-3}, pages = {77-87}, url = {http://www.sciencedirect.com/science/article/pii/S0010465599002581}, keyword = {} }

58 - A generalized sampling theory without bandlimiting constraints. M. Unser and J. Zerubia. IEEE Trans. on Circuits And Systems II, 45(8): pages 959-969, August 1998. @ARTICLE{jz98b, author = {Unser, M. and Zerubia, J.}, title = {A generalized sampling theory without bandlimiting constraints}, year = {1998}, month = {August}, journal = {IEEE Trans. on Circuits And Systems II}, volume = {45}, number = {8}, pages = {959-969}, pdf = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=718806}, keyword = {} }

59 - New Prospects in Line Detection by Dynamic Programming. N. Merlet and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(4): pages 426-431, April 1996. Keywords : Line detection, dynamic programming, energy minimization, curvature, satellite images. @ARTICLE{MerletPAMI96, author = {Merlet, N. and Zerubia, J.}, title = {New Prospects in Line Detection by Dynamic Programming}, year = {1996}, month = {April}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {18}, number = {4}, pages = {426-431}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=10562&arnumber=491623&count=15&index=6}, keyword = {Line detection, dynamic programming, energy minimization, curvature, satellite images} } Abstract : The detection of lines in satellite images has drawn a lot of attention within the last 15 years. Problems of resolution, noise, and image understanding are involved, and one of the best methods developed so far is the F* algorithm of Fischler, which achieves robustness, rightness, and rapidity. Like other methods of dynamic programming, it consists of defining a cost which depends on local information; then a summation-minimization process in the image is performed. We present herein a mathematical formalization of the F* algorithm, which allows us to extend the cost both to cliques of more than two points (to deal with the contrast), and to neighborhoods of size larger than one (to take into account the curvature). Thus, all the needed information (contrast, grey-level, curvature) is synthesized in a unique cost function defined on the digital original image. This cost is used to detect roads and valleys in satellite images (SPOT).

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173 Conference articles

1 - Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test. V. Krylov and G. Moser and A. Voisin and S.B. Serpico and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Orlando, United States, October 2012. @INPROCEEDINGS{ICIP12, author = {Krylov, V. and Moser, G. and Voisin, A. and Serpico, S.B. and Zerubia, J.}, title = {Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test}, year = {2012}, month = {October}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Orlando, United States}, url = {http://hal.inria.fr/hal-00724284}, keyword = {} }

2 - Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In EURASIP, Bucarest, Romania, August 2012. @INPROCEEDINGS{EURASIP12, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model}, year = {2012}, month = {August}, booktitle = {EURASIP}, address = {Bucarest, Romania}, url = {http://hal.inria.fr/hal-00723286}, keyword = {} }

3 - A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction. K. Kayabol and J. Zerubia. In IEEE International Geoscience and Remote Sensing Symposium, pages 4054-4057, Munich, Germany, July 2012. @INPROCEEDINGS{IGARSS12, author = {Kayabol, K. and Zerubia, J.}, title = {A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction}, year = {2012}, month = {July}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, pages = {4054-4057}, address = {Munich, Germany}, url = {http://hal.inria.fr/hal-00697172}, keyword = {} }

4 - An hierarchical approach for model-based classification of SAR images. K. Kayabol and J. Zerubia. In 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, April 2012. @INPROCEEDINGS{Kayabol12, author = {Kayabol, K. and Zerubia, J.}, title = {An hierarchical approach for model-based classification of SAR images}, year = {2012}, month = {April}, booktitle = {20th Signal Processing and Communications Applications Conference}, address = {Mugla, Turkey}, url = {http://hal.inria.fr/hal-00686658}, keyword = {} }

5 - Multichannel hierarchical image classification using multivariate copulas. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In IS&T/SPIE Electronic Imaging – Computational Imaging X, San Francisco, United States, January 2012. @INPROCEEDINGS{SPIE12, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel hierarchical image classification using multivariate copulas}, year = {2012}, month = {January}, booktitle = {IS&T/SPIE Electronic Imaging – Computational Imaging X}, address = {San Francisco, United States}, url = {http://dx.doi.org/10.1117/12.917298}, keyword = {} }

6 - Synthetic Aperture Radar Image Classification via Mixture Approaches. V. Krylov and J. Zerubia. In Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, November 2011. Keywords : Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution. Copyright : IEEE @INPROCEEDINGS{krylovCOMCAS11, author = {Krylov, V. and Zerubia, J.}, title = {Synthetic Aperture Radar Image Classification via Mixture Approaches}, year = {2011}, month = {November}, booktitle = {Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, address = {Tel Aviv, Israel}, url = {http://www.ortra.biz/comcas/}, pdf = {http://hal.archives-ouvertes.fr/inria-00625551/en/}, keyword = {Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} } Abstract : In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors.

7 - Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil. J. Zhou and C. Proisy and X. Descombes and J. Zerubia and G. Le Maire and Y. Nouvellon and P. Couteron. In Asian Conference on Pattern Recognition (ACPR), Beijing, China, November 2011. Keywords : tree detection, Eucalyptus plantation, Marked point process, multi-date detection. @INPROCEEDINGS{Zhou11, author = {Zhou, J. and Proisy, C. and Descombes, X. and Zerubia, J. and Le Maire, G. and Nouvellon, Y. and Couteron, P.}, title = {Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil}, year = {2011}, month = {November}, booktitle = {Asian Conference on Pattern Recognition (ACPR)}, address = {Beijing, China}, url = {http://hal.inria.fr/hal-00740973}, keyword = {tree detection, Eucalyptus plantation, Marked point process, multi-date detection} } Abstract : Individual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations.

8 - Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images. S. Prigent and D. Zugaj and X. Descombes and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. @INPROCEEDINGS{prigent11a, author = {Prigent, S. and Zugaj, D. and Descombes, X. and Martel, P. and Zerubia, J.}, title = {Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, pdf = {http://hal.inria.fr/docs/00/59/06/94/PDF/icip_final.pdf}, keyword = {} } Abstract : Clinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy.

9 - A fast multiple birth and cut algorithm using belief propagation. A. Gamal Eldin and X. Descombes and Charpiat G. and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. Keywords : Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation. @INPROCEEDINGS{MBC_ICIP11, author = {Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, title = {A fast multiple birth and cut algorithm using belief propagation}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592446/fr/}, keyword = {Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation} } Abstract : In this paper, we present a faster version of the newly proposed Multiple Birth and Cut (MBC) algorithm. MBC is an optimization method applied to the energy minimization of an object based model, defined by a marked point process. We show that, by proposing good candidates in the birth step of this algorithm, the speed of convergence is increased. The algorithm starts by generating a dense configuration in a special organization, the best candidates are selected using the belief propagation algorithm. Next, this candidate configuration is combined with the current configuration using binary graph cuts as presented in the original version of the MBC algorithm. We tested the performance of our algorithm on the particular problem of counting flamingos in a colony, and show that it is much faster with the modified birth step.

10 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities. K. Kayabol and A. Voisin and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, September 2011. Keywords : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm. @INPROCEEDINGS{inria-00592252, author = {Kayabol, K. and Voisin, A. and Zerubia, J.}, title = {SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, pages = {173-176}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592252/en/}, keyword = {High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} } Abstract : We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data.

11 - Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution. A. Voisin and V. Krylov and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, September 2011. Keywords : SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models. @INPROCEEDINGS{VoisinGretsi2011, author = {Voisin, A. and Krylov, V. and Zerubia, J.}, title = {Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux}, url = {http://hal.inria.fr/inria-00623003/fr/}, keyword = {SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models} } Résumé : Ce papier présente un modèle de classification bayésienne supervisée d’images acquises par Radar à Synthèse d’Ouverture (RSO) très haute résolution en polarisation simple contenant des zones urbaines, particulièrement affectées par le bruit de chatoiement. Ce modèle prend en compte à la fois une représentation statistique des images RSO par modèle de mélanges finis et de copules, et une modélisation contextuelle à partir de champs de Markov hiérarchiques. Abstract : This paper deals with the Bayesian classification of single-polarized very high resolution synthetic aperture radar (SAR) images that depict urban areas. The difficulty of such a classification relies in the significant effects of speckle noise. The model considered here takes into account both statistical modeling of images via finite mixture models and copulas, and contextual modeling thanks to hierarchical Markov random fields

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

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.

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.

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.

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.

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.

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.

22 - Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model. A. El Ghoul and I. H. Jermyn and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010. Keywords : Phase Field, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing. Copyright : ISPRS @INPROCEEDINGS{Elghoul10a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model}, year = {2010}, month = {September}, booktitle = {Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, address = {Paris, France}, pdf = {https://hal.inria.fr/inria-00491017}, keyword = {Phase Field, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing} } Abstract : We propose a new algorithm for network segmentation from VHR remote sensing images. The algorithm performs this task quasi-automatically, that is, with no human intervention except to fix some parameters. The task is made difficult by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which branches do not come to an end, hence tending to close gaps in the network; and in which junctions show approximate ‘conservation of width’. We also introduce image models for network and background, which are validated using maximum likelihood segmentation against other possibilities. We then test the full model on VHR optical and multispectral satellite images.

23 - Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features. A. Voisin and G. Moser and V. Krylov and S.B. Serpico and J. Zerubia. In Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, Toulouse, France, September 2010. Keywords : SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields. Copyright : SPIE @INPROCEEDINGS{7830-23, author = {Voisin, A. and Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, title = {Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features}, year = {2010}, month = {September}, booktitle = {Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, volume = {7830}, address = {Toulouse, France}, url = {http://hal.archives-ouvertes.fr/inria-00516333/en}, pdf = {http://hal.archives-ouvertes.fr/docs/00/51/63/33/PDF/Classification_of_VHR_SAR_SPIE_sept2010_Toulouse_Voisin.pdf}, keyword = {SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields} } Abstract : This paper addresses the problem of the classification of very high resolution SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed images point out the accuracy of the proposed method, also as compared with previous contextual classifiers.

24 - Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles. C. Benedek and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, August 2010. Keywords : Marked point process, multiple birth-and-death dynamics, Building extraction. @INPROCEEDINGS{benedekICPR10, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles}, year = {2010}, month = {August}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Istanbul, Turkey}, pdf = {http://hal.archives-ouvertes.fr/inria-00481019/en/}, keyword = {Marked point process, multiple birth-and-death dynamics, Building extraction} } Abstract : In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques.

25 - Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), Vol. 1305, pages 319-326, Chamonix, France, July 2010. Keywords : multichannel SAR, Classification, probability density function estimation, Markov random field, copula. Copyright : AIP @INPROCEEDINGS{krylovMaxEnt10, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields}, year = {2010}, month = {July}, booktitle = {Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)}, volume = {1305}, pages = {319-326}, address = {Chamonix, France}, url = {http://hal.archives-ouvertes.fr/inria-00495557/en/}, pdf = {http://hal.archives-ouvertes.fr/docs/00/49/55/57/PDF/krylov_MaxEnt2010.pdf}, keyword = {multichannel SAR, Classification, probability density function estimation, Markov random field, copula} } Abstract : The last decades have witnessed an intensive development and a significant increase of interest to remote sensing, and, in particular, to synthetic aperture radar (SAR) imagery. In this paper we develop a supervised classification approach for medium and high resolution multichannel SAR amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and the Markov random field approach to Bayesian image classification. The finite mixture modeling is done via a recently proposed SAR-specific dictionary-based stochastic expectation maximization approach to class-conditional amplitude probability density function estimation, which is applied separately to all the SAR channels. For modeling the class-conditional joint distributions of multichannel data the statistical concept of copulas is employed, and a dictionary-based copula selection method is proposed. Finally, the Markov random field approach enables to take into account the contextual information and to gain robustness against the inherent noise-like phenomenon of SAR known as speckle. The designed method is an extension and a generalization to multichannel SAR of a recently developed single-channel and Dual-pol SAR image classification technique. The accuracy of the developed multichannel SAR classification approach is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques.

26 - Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification. S. Prigent and X. Descombes and D. Zugaj and J. Zerubia. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, June 2010. Keywords : Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation. @INPROCEEDINGS{sp01, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Zerubia, J.}, title = {Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification}, year = {2010}, month = {June}, booktitle = {Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)}, address = {Reykjavik, Iceland}, pdf = {http://hal.inria.fr/docs/00/49/55/60/PDF/whispers2010_submission_124.pdf}, keyword = {Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation} } Abstract : Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.

27 - Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours. M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IS&T/SPIE Electronic Imaging, San Jose, USA, January 2010. Keywords : Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics. Copyright : Copyright 2010 by SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE Electronic Imaging 2010 Conference in San Jose, USA, and is made available as an electronic reprint with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. @INPROCEEDINGS{Kulikova10a, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours}, year = {2010}, month = {January}, booktitle = {Proc. IS&T/SPIE Electronic Imaging}, address = {San Jose, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/46/54/72/PDF/Kulikova_SPIE2010.pdf}, keyword = {Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics} } Abstract : We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. From an alternative point of view, the approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined on the configuration space of an arbitrary number of objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data.

28 - High resolution SAR-image classification by Markov random fields and finite mixtures. G. Moser and V. Krylov and S.B. Serpico and J. Zerubia. In Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010), Vol. 7533, pages 753308, San Jose, USA, January 2010. Keywords : SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula. Copyright : SPIE @INPROCEEDINGS{moserSPIE2010a, author = {Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, title = {High resolution SAR-image classification by Markov random fields and finite mixtures}, year = {2010}, month = {January}, booktitle = {Proc. of SPIE (IS&T/SPIE Electronic Imaging 2010)}, volume = {7533}, pages = {753308}, address = {San Jose, USA}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=776565}, pdf = {http://hal.archives-ouvertes.fr/inria-00442348/en/}, keyword = {SAR image classification, Dictionary, amplitude probability density, Stochastic EM (SEM), Markov random field, copula} } Abstract : In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for SAR amplitude probability density function estimation. For modeling the joint distribution from marginals corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic supervised algorithm is validated in the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed.

29 - A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects. M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IEEE SITIS, Publ. IEEE Computer Society, Marrakech, Maroc, December 2009. Keywords : Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics. @INPROCEEDINGS{Kulikova09a, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects}, year = {2009}, month = {December}, booktitle = {Proc. IEEE SITIS}, publisher = {IEEE Computer Society}, address = {Marrakech, Maroc}, pdf = {http://hal.inria.fr/docs/00/43/63/20/PDF/PID1054029.pdf}, keyword = {Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics} } Abstract : We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns.

30 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics. C. Benedek and X. Descombes and J. Zerubia. In IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, December 2009. Keywords : Marked point process, Change detection, Aerial images, Building extraction, Satellite images. @INPROCEEDINGS{benedekWacv09, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, year = {2009}, month = {December}, booktitle = {IEEE Workshop on Applications of Computer Vision (WACV)}, pages = {100-105}, address = {Snowbird, Utah, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, keyword = {Marked point process, Change detection, Aerial images, Building extraction, Satellite images} } Abstract : In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features.

31 - Object extraction from high resolution SAR images using a birth and death dynamics. F. Arslan and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : High resolution SAR images, Object extraction, Marked point process, birth and death process. @INPROCEEDINGS{Fatih09, author = {Arslan, F. and Descombes, X. and Zerubia, J.}, title = {Object extraction from high resolution SAR images using a birth and death dynamics}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413907}, keyword = {High resolution SAR images, Object extraction, Marked point process, birth and death process} } Abstract : We present a new approach to extract predefined objects, such as trees and oil tanks for instance, from high resolution SAR images. We consider a stochastic approach based on an object process also called marked point process. The objects represent trees or oil tanks which are modeled by disks in the image. We first define a Gibbs density that takes into account both prior information and the data. The energy we define is composed of two terms, one is a prior, penalizing overlaps between objects, and the other is a data term, which measures the suitability of an object in the SAR image. The problem is then reduced to an energy minimization problem. We sample the process to extract the configuration of objects minimizing the energy by a fast birth-and-death dynamics, leading to the total number of objects (trees or oil tanks in our case). This approach is much faster than manual counts and does not need any preprocessing or supervision of a user.

32 - Multi-class SVM for forestry classification. N. Hajj Chehade and JG. Boureau and C. Vidal and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009. Keywords : Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation. @INPROCEEDINGS{Nabil09, author = {Hajj Chehade, N. and Boureau, JG. and Vidal, C. and Zerubia, J.}, title = {Multi-class SVM for forestry classification}, year = {2009}, month = {November}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Cairo, Egypt}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413395}, keyword = {Support Vector Machines, texture segmentation, Haralick feature, remote sensing, Forest vegetation} } Abstract : In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM), which is a popular supervised learning technique for classification. The choice of features (along with their corresponding parameters), the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done on several CIR aerial images provided by the French National Forest Inventory (IFN). In this paper, we will show one example on a national forest near Sedan (in France), and compare our result with the IFN map.

33 - Estimation des paramètres de processus ponctuels marqués dans le cadre de l'extraction d’objets en imagerie de télédétection. F. Chatelain and X. Descombes and J. Zerubia. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, November 2009. @INPROCEEDINGS{cha09a, author = {Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Estimation des paramètres de processus ponctuels marqués dans le cadre de l'extraction d’objets en imagerie de télédétection}, year = {2009}, month = {November}, booktitle = {Proc. Symposium on Signal and Image Processing (GRETSI)}, address = {Dijon, France}, url = {http://hal.inria.fr/inria-00399258/fr/}, keyword = {} }

34 - A phase field higher-order active contour model of directed networks. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV, Kyoto, Japan, September 2009. Keywords : Geometric prior, Shape, Higher-order actif contours, Phase Field, Directed networks. Copyright : ©2009 IEEE. @INPROCEEDINGS{ElGhoul09b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A phase field higher-order active contour model of directed networks}, year = {2009}, month = {September}, booktitle = {2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV}, address = {Kyoto, Japan}, url = {https://hal.inria.fr/inria-00409910}, pdf = {http://hal.inria.fr/docs/00/40/99/10/PDF/nordia09aymenelghoul.pdf}, keyword = {Geometric prior, Shape, Higher-order actif contours, Phase Field, Directed networks} } Abstract : The segmentation of directed networks is an important problem in many domains, e.g. medical imaging (vascular networks) and remote sensing (river networks). Directed networks carry a unidirectional flow in each branch, which leads to characteristic geometric properties. In this paper, we present a nonlocal phase field model of directed networks. In addition to a scalar field representing a region by its smoothed characteristic function and interacting nonlocally so as to favour network configurations, the model contains a vector field representing the ‘flow’ through the network branches. The vector field is strongly encouraged to be zero outside, and of unit magnitude inside the region; and to have zero divergence. This prolongs network branches; controls width variation along a branch; and produces asymmetric junctions for which total incoming branch width approximately equals total outgoing branch width. In conjunction with a new interaction function, it also allows a broad range of stable branch widths. We analyse the energy to constrain the parameters, and show geometric experiments confirming the above behaviour. We also show a segmentation result on a synthetic river image.

35 - Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes. M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. Symposium on Signal and Image Processing (GRETSI), Dijon, France, September 2009. Keywords : Deconvolution, nesterov scheme, Wavelets, l1 norm. @INPROCEEDINGS{GRETSICarlavan09, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, title = {Algorithme rapide pour la restauration d'image régularisée sur les coefficients d'ondelettes}, year = {2009}, month = {September}, booktitle = {Proc. Symposium on Signal and Image Processing (GRETSI)}, address = {Dijon, France}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/CarlavanGretsi09.pdf}, keyword = {Deconvolution, nesterov scheme, Wavelets, l1 norm} } Résumé : De nombreuses méthodes de restauration d'images consistent à minimiser une énergie convexe. Nous nous focalisons sur l'utilisation de ces méthodes et considérons la minimisation de deux critères contenant une norme l1 des coefficients en ondelettes. La plupart des travaux publiés récemment proposent un critère à minimiser dans le domaine des coefficients en ondelettes, utilisant ainsi un a priori de parcimonie. Nous proposons un algorithme rapide et des résultats de déconvolution par minimisation d'un critère dans le domaine image, avec un a priori de régularité exprimé dans le domaine image utilisant une décomposition redondante sur une trame. L'algorithme et le modèle proposés semblent originaux pour ce problème en traitement d'images et sont performants en terme de temps de calculs et de qualité de restauration. Nous montrons des comparaisons entre les deux types d' a priori. Abstract : Many image restoration techniques are based on convex energy minimization. We focus on the use of these techniques and consider the minimization of two criteria holding a l1-norm of wavelet coefficients. Most of the recent research works are based on the minimization of a criterion in the wavelet coefficients domain, namely as a sparse prior. We propose a fast algorithm and deconvolution results obtained by minimizing a criterion in the image domain using a redundant decomposition on a frame. The algorithm and model proposed are unusual for this problem and very efficient in term of computing time and quality of restoration results. We show comparisons between the two different priors.

36 - Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images. A. El Ghoul and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 2009. Keywords : Geometric prior, Shape, Higher-order active contour, Phase Field, remote sensing. Copyright : EURASIP @INPROCEEDINGS{ElGhoul09a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images}, year = {2009}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Glasgow, Scotland}, url = {http://hal.inria.fr/inria-00390446/fr/}, pdf = {http://hal.inria.fr/docs/00/39/04/46/PDF/eusipco09aymenelghoul.pdf}, keyword = {Geometric prior, Shape, Higher-order active contour, Phase Field, remote sensing} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. We incorporate such knowledge via phase fields and higher-order active contours (HOACs). In this paper: we introduce an improved prior model, the phase field HOAC ‘inflection point’ model of a network; we present an improved data term for the segmentation of road networks; we confirm the robustness of the resulting model to choice of gradient descent initialization; and we illustrate these points via road network extraction results on VHR satellite images.

37 - Parameter estimation for marked point processes. Application to object extraction from remote sensing images. (poster). F. Chatelain and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Bonn, Germany, August 2009. @INPROCEEDINGS{ChatelainEMMCVPR09, author = {Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Parameter estimation for marked point processes. Application to object extraction from remote sensing images. (poster)}, year = {2009}, month = {August}, booktitle = {Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, address = {Bonn, Germany}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-03641-5_17}, keyword = {} }

38 - Complex wavelet regularization for solving inverse problems in remote sensing. M. Carlavan and P. Weiss and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009. Keywords : Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet. @INPROCEEDINGS{IGARSSCarlavan09, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L. and Zerubia, J.}, title = {Complex wavelet regularization for solving inverse problems in remote sensing}, year = {2009}, month = {July}, booktitle = {Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS)}, address = {Cape Town, South Africa}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/IGARSS09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/IGARSS09.pdf}, keyword = {Deconvolution, Dual smoothing, nesterov scheme, remote sensing, wavelet} } Abstract : Many problems in remote sensing can be modeled as the minimization of the sum of a data term and a prior term. We propose to use a new complex wavelet based prior and an efficient scheme to solve these problems. We show some results on a problem of image reconstruction with noise, irregular sampling and blur. We also show a comparison between two widely used priors in image processing: sparsity and regularity priors.

39 - Point-spread function retrieval for fluorescence microscopy. P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Publ. IEEE, Org. IEEE, Boston, USA, June 2009. Keywords : fluorescence microscopy, point spread function, EM algorithm, Deconvolution. Copyright : Copyright 2009 IEEE. Published in the 2009 International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2009), scheduled for June 28 - July 1, 2009 in Boston, Massachusetts, U.S.A. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966. @INPROCEEDINGS{ppankajakshan09a, author = {Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, title = {Point-spread function retrieval for fluorescence microscopy}, year = {2009}, month = {June}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, organization = {IEEE}, address = {Boston, USA}, pdf = {http://hal.inria.fr/docs/00/39/55/34/PDF/pankajakshan.pdf}, keyword = {fluorescence microscopy, point spread function, EM algorithm, Deconvolution} } Abstract : In this paper we propose a method for retrieving the Point-Spread Function (PSF) of an imaging system given the observed images of fluorescent microspheres. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory. The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data.

40 - Smoothing techniques for convex problems. Applications in image processing. P. Weiss and M. Carlavan and L. Blanc-Féraud and J. Zerubia. In Proc. SAMPTA (international conference on Sampling Theory and Applications), Marseille, France, May 2009. Keywords : nesterov scheme, convergence rate, Dual smoothing. @INPROCEEDINGS{PWEISS_SAMPTA09, author = {Weiss, P. and Carlavan, M. and Blanc-Féraud, L. and Zerubia, J.}, title = {Smoothing techniques for convex problems. Applications in image processing}, year = {2009}, month = {May}, booktitle = {Proc. SAMPTA (international conference on Sampling Theory and Applications)}, address = {Marseille, France}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Eusipco09.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/Sampta09.pdf}, keyword = {nesterov scheme, convergence rate, Dual smoothing} } Abstract : In this paper, we present two algorithms to solve some inverse problems coming from the field of image processing. The problems we study are convex and can be expressed simply as sums of lp-norms of affine transforms of the image. We propose 2 different techniques. They are - to the best of our knowledge - new in the domain of image processing and one of them is new in the domain of mathematical programming. Both methods converge to the set of minimizers. Additionally, we show that they converge at least as O(1/N) (where N is the iteration counter) which is in some sense an ``optimal'' rate of convergence. Finally, we compare these approaches to some others on a toy problem of image super-resolution with impulse noise.

41 - Dictionary-based probability density function estimation for high-resolution SAR data. V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009), Vol. 7246, pages 72460S, San Jose, USA, January 2009. Keywords : SAR image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM). Copyright : SPIE @INPROCEEDINGS{KrylovSPIE09, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Dictionary-based probability density function estimation for high-resolution SAR data}, year = {2009}, month = {January}, booktitle = {Proc. of SPIE (IS&T/SPIE Electronic Imaging 2009)}, volume = {7246}, pages = {72460S}, address = {San Jose, USA}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=812524}, pdf = {http://hal.archives-ouvertes.fr/inria-00361384/en/}, keyword = {SAR image, Probability density function, parametric estimation, finite mixture models, Stochastic EM (SEM)} } Abstract : In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images. This method is an extension of previously existing method for lower resolution images. The method integrates the stochastic expectation maximization (SEM) scheme and the method of log-cumulants (MoLC) with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). The proposed dictionary consists of eight state-of-the-art SAR- specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The designed scheme is endowed with the novel initialization procedure and the algorithm to automatically estimate the optimal number of mixture components. The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive accuracy measures such as correlation coefficient (above 99,5%). The method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous scenes.

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