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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 13--22, 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 |
= |
{13--22}, |
| 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 25--42, 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 |
= |
{25--42}, |
| url |
= |
{http://documents.irevues.inist.fr/handle/2042/2216}, |
| keyword |
= |
{} |
| } |
|
43 - Satellite image deblurring using complex wavelet packets.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. International Journal of Computer Vision, 51(3): pages 205--217, 2003.
@ARTICLE{JalobeaLBFJZ,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, 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 |
= |
{205--217}, |
| pdf |
= |
{http://link.springer.com/article/10.1023/A%3A1021801918603}, |
| keyword |
= |
{} |
| } |
|
44 - Skewed alpha-stable distributions for modelling textures.
E.E. Kuruoglu and J. Zerubia. Pattern Recognition Letters, 24(1-3): pages 339--348, 2003.
@ARTICLE{Kuruoglu03a,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Skewed alpha-stable distributions for modelling textures}, |
| year |
= |
{2003}, |
| journal |
= |
{Pattern Recognition Letters}, |
| volume |
= |
{24}, |
| number |
= |
{1-3}, |
| pages |
= |
{339--348}, |
| 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 77-84, 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 |
= |
{77-84}, |
| 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. Blanc-Féraud and J. Zerubia. Pattern Recognition, 35(2): pages 341--352, 2002.
@ARTICLE{jalo02h,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, 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 |
= |
{341--352}, |
| 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(1-2): pages 149-156, 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 |
= |
{1-2}, |
| pages |
= |
{149-156}, |
| url |
= |
{http://www.degruyter.com/view/j/mcma.2001.7.issue-1-2/mcma.2001.7.1-2.149/mcma.2001.7.1-2.149.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 195-211, 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 |
= |
{195-211}, |
| 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 (CNN-UM), 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 CNN-UM. We can introduce the whole pseudo-stochastic segmentation process in the CNN architecture using 8 memories/cell. We use simple arithmetic functions (addition, multiplication), equality-test between neighboring pixels and very simple nonlinear output functions (step, jigsaw). With this architecture, the proposed VLSI CNN chip can execute a pseudo-stochastic relaxation algorithm of about 100 iterations in about 100 μs.
In the suggested solution the segmentation is unsupervised, where a pixel-level statistical estimation model is used. We have tested different monogrid and multigrid architectures.
In our CNN-UM model several complex preprocessing steps can be involved, such as texture-classification or anisotropic diffusion. With these preprocessing steps, our fully parallel cellular system may work as a high-level image segmentation machine, using only simple functions based on the close-neighborhood of a pixel. |
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