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Publications of Josiane Zerubia
Result of the query in the list of publications :
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. |
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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. |
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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 |
= |
{} |
| } |
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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 |
= |
{} |
| } |
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