
Publications of Josiane Zerubia
Result of the query in the list of publications :
59 Articles 
41  Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet. C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 170: pages 1322, 2003.
@ARTICLE{lacostesfpt,

author 
= 
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, 
title 
= 
{Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet}, 
year 
= 
{2003}, 
journal 
= 
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, 
volume 
= 
{170}, 
pages 
= 
{1322}, 
url 
= 
{http://www.researchgate.net/profile/Nicolas_Baghdadi/publication/236882132_Extraction_automatique_des_rseaux_liniques__partir_dimages_satellitaires_et_ariennes_par_processus_Markov_objets/links/00463519e05ebd9e83000000.pdf?disableCoverPage=true}, 
keyword 
= 
{} 
} 

42  Classification de Textures Hyperspectrales Fondée sur un Modèle Markovien et Une Technique de Poursuite de Projection. G. Rellier and X. Descombes and F. Falzon and J. Zerubia. Traitement du Signal, 20(1): pages 2542, 2003.
@ARTICLE{rellierXDFFJZ,

author 
= 
{Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, 
title 
= 
{Classification de Textures Hyperspectrales Fondée sur un Modèle Markovien et Une Technique de Poursuite de Projection}, 
year 
= 
{2003}, 
journal 
= 
{Traitement du Signal}, 
volume 
= 
{20}, 
number 
= 
{1}, 
pages 
= 
{2542}, 
url 
= 
{http://documents.irevues.inist.fr/handle/2042/2216}, 
keyword 
= 
{} 
} 

43  Satellite image deblurring using complex wavelet packets. A. Jalobeanu and L. BlancFéraud and J. Zerubia. International Journal of Computer Vision, 51(3): pages 205217, 2003.
@ARTICLE{JalobeaLBFJZ,

author 
= 
{Jalobeanu, A. and BlancFéraud, L. and Zerubia, J.}, 
title 
= 
{Satellite image deblurring using complex wavelet packets}, 
year 
= 
{2003}, 
journal 
= 
{International Journal of Computer Vision}, 
volume 
= 
{51}, 
number 
= 
{3}, 
pages 
= 
{205217}, 
pdf 
= 
{http://link.springer.com/article/10.1023/A%3A1021801918603}, 
keyword 
= 
{} 
} 

44  Skewed alphastable distributions for modelling textures. E.E. Kuruoglu and J. Zerubia. Pattern Recognition Letters, 24(13): pages 339348, 2003.
@ARTICLE{Kuruoglu03a,

author 
= 
{Kuruoglu, E.E. and Zerubia, J.}, 
title 
= 
{Skewed alphastable distributions for modelling textures}, 
year 
= 
{2003}, 
journal 
= 
{Pattern Recognition Letters}, 
volume 
= 
{24}, 
number 
= 
{13}, 
pages 
= 
{339348}, 
url 
= 
{http://www.sciencedirect.com/science/article/pii/S0167865502002477}, 
keyword 
= 
{} 
} 

45  Marked Point Processes in Image Analysis. X. Descombes and J. Zerubia. IEEE Signal Processing Magazine, 19(5): pages 7784, September 2002.
@ARTICLE{XDJZ,

author 
= 
{Descombes, X. and Zerubia, J.}, 
title 
= 
{Marked Point Processes in Image Analysis}, 
year 
= 
{2002}, 
month 
= 
{September}, 
journal 
= 
{IEEE Signal Processing Magazine}, 
volume 
= 
{19}, 
number 
= 
{5}, 
pages 
= 
{7784}, 
pdf 
= 
{http://ieeexplore.ieee.org/iel5/79/22084/01028354.pdf?tp=&arnumber=1028354&isnumber=22084}, 
keyword 
= 
{} 
} 

46  Extension of phase correlation to subpixel registration. H. Foroosh and J. Zerubia and M. Berthod. IEEE Trans. on Image Processing, 11(3): pages 188  200, March 2002.
@ARTICLE{forooshjzmb,

author 
= 
{Foroosh, H. and Zerubia, J. and Berthod, M.}, 
title 
= 
{Extension of phase correlation to subpixel registration}, 
year 
= 
{2002}, 
month 
= 
{March}, 
journal 
= 
{IEEE Trans. on Image Processing}, 
volume 
= 
{11}, 
number 
= 
{3}, 
pages 
= 
{188  200}, 
pdf 
= 
{http://ieeexplore.ieee.org/iel5/83/21305/00988953.pdf?tp=&arnumber=988953&isnumber=21305}, 
keyword 
= 
{} 
} 

47  Local registration and deformation of a road cartographic database on a SPOT Satellite Image. G. Rellier and X. Descombes and J. Zerubia. Pattern Recognition, 35(10), 2002.
@ARTICLE{rellierXDJZ,

author 
= 
{Rellier, G. and Descombes, X. and Zerubia, J.}, 
title 
= 
{Local registration and deformation of a road cartographic database on a SPOT Satellite Image}, 
year 
= 
{2002}, 
journal 
= 
{Pattern Recognition}, 
volume 
= 
{35}, 
number 
= 
{10}, 
url 
= 
{http://www.sciencedirect.com/science/article/pii/S0031320301001807}, 
keyword 
= 
{} 
} 

48  Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method. A. Jalobeanu and L. BlancFéraud and J. Zerubia. Pattern Recognition, 35(2): pages 341352, 2002.
@ARTICLE{jalo02h,

author 
= 
{Jalobeanu, A. and BlancFéraud, L. and Zerubia, J.}, 
title 
= 
{Hyperparameter estimation for satellite image restoration using a MCMC Maximum Likelihood method}, 
year 
= 
{2002}, 
journal 
= 
{Pattern Recognition}, 
volume 
= 
{35}, 
number 
= 
{2}, 
pages 
= 
{341352}, 
url 
= 
{http://www.sciencedirect.com/science/article/pii/S0031320300001783}, 
keyword 
= 
{} 
} 

49  A RJMCMC algorithm for object processes in image processing. X. Descombes and R. Stoica and L. Garcin and J. Zerubia. Monte Carlo Methods and Applications, 7(12): pages 149156, 2001.
@ARTICLE{xd01c,

author 
= 
{Descombes, X. and Stoica, R. and Garcin, L. and Zerubia, J.}, 
title 
= 
{A RJMCMC algorithm for object processes in image processing}, 
year 
= 
{2001}, 
journal 
= 
{Monte Carlo Methods and Applications}, 
volume 
= 
{7}, 
number 
= 
{12}, 
pages 
= 
{149156}, 
url 
= 
{http://www.degruyter.com/view/j/mcma.2001.7.issue12/mcma.2001.7.12.149/mcma.2001.7.12.149.xml}, 
keyword 
= 
{} 
} 

50  Image segmentation using Markov random field model in fully parallel cellular network architectures. T. Szirányi and J. Zerubia and L. Czúni and D. Geldreich and Z. Kato. Real Time Imaging, 6(3): pages 195211, June 2000.
@ARTICLE{jz00y,

author 
= 
{Szirányi, T. and Zerubia, J. and Czúni, L. and Geldreich, D. and Kato, Z.}, 
title 
= 
{Image segmentation using Markov random field model in fully parallel cellular network architectures}, 
year 
= 
{2000}, 
month 
= 
{June}, 
journal 
= 
{Real Time Imaging}, 
volume 
= 
{6}, 
number 
= 
{3}, 
pages 
= 
{195211}, 
pdf 
= 
{http://dx.doi.org/10.1006/rtim.1998.0159}, 
keyword 
= 
{} 
} 
Abstract :
Markovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imaging VLSI chips.
As an example, we have developed a simplified statistical image segmentation algorithm for the Cellular Neural/Nonlinear Networks Universal Machine (CNNUM), which is a new image processing tool, containing thousands of cells with analog dynamics, local memories and processing units. The Modified Metropolis Dynamics (MMD) optimization method can be implemented into the raw analog architecture of the CNNUM. We can introduce the whole pseudostochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equalitytest between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudostochastic relaxation algorithm of about 100 iterations in about 100 μs.
In the suggested solution the segmentation is unsupervised, where a pixellevel statistical estimation model is used. We have tested different monogrid and multigrid architectures.
In our CNNUM model several complex preprocessing steps can be involved, such as textureclassification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a highlevel image segmentation machine, using only simple functions based on the closeneighborhood of a pixel. 

51  A variational model for image classification and restoration. C. Samson and L. BlancFéraud and G. Aubert and J. Zerubia. IEEE Trans. Pattern Analysis ans Machine Intelligence, 22(5): pages 460472, May 2000.
@ARTICLE{cs00,

author 
= 
{Samson, C. and BlancFéraud, 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 
= 
{460472}, 
pdf 
= 
{http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=857003}, 
keyword 
= 
{} 
} 

52  A Level Set Model for Image Classification. C. Samson and L. BlancFéraud and G. Aubert and J. Zerubia. International Journal of Computer Vision, 40(3): pages 187198, 2000.
@ARTICLE{cs00b,

author 
= 
{Samson, C. and BlancFéraud, 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 
= 
{187198}, 
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 2132, 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 
= 
{2132}, 
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 221236, 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 
= 
{221236}, 
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 954963, 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 
= 
{954963}, 
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 modelsthe standard Potts model, an inhomogeneous variation of the Potts model, and a longrange interaction model, better adapted to modeling realworld 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 591604, 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 
= 
{591604}, 
pdf 
= 
{http://dx.doi.org/10.1016/S00313203(98)001046}, 
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(13): pages 7787, 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 
= 
{13}, 
pages 
= 
{7787}, 
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 959969, 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 
= 
{959969}, 
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 426431, 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 
= 
{426431}, 
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 summationminimization 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, greylevel, 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/hal00724284}, 
keyword 
= 
{} 
} 

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