|
|
The Publications
Result of the query in the list of publications :
101 Articles |
51 - Detecting codimension-two objects in an image with Ginzburg-Landau models.
G. Aubert and J.F. Aujol and L. Blanc-Féraud. International Journal of Computer Vision, 65(1-2): pages 29-42, November 2005.
Keywords : Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images.
@ARTICLE{laure-ijcv05,
|
| author |
= |
{Aubert, G. and Aujol, J.F. and Blanc-Féraud, L.}, |
| title |
= |
{Detecting codimension-two objects in an image with Ginzburg-Landau models}, |
| year |
= |
{2005}, |
| month |
= |
{November}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{65}, |
| number |
= |
{1-2}, |
| pages |
= |
{29-42}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/GL_IJCV_5.pdf}, |
| keyword |
= |
{Ginzburg-Landau model, Point Detection, Segmentation, PDE, Biological images, SAR Images} |
| } |
Abstract :
In this paper, we propose a new mathematical model for detecting in an image singularities of codimension greater than or equal to two. This means we want to detect points in a 2-D image or points and curves in a 3-D image. We drew one's inspiration from
Ginzburg-Landau (G-L) models which have proved their efficiency for modeling many phenomena in physics. We introduce the model, state its
mathematical properties and give some experimental results demonstrating its capability in image processing. |
|
52 - Point Processes for Unsupervised Line Network Extraction in Remote Sensing.
C. Lacoste and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, October 2005.
@ARTICLE{lacoste05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Point Processes for Unsupervised Line Network Extraction in Remote Sensing}, |
| year |
= |
{2005}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{27}, |
| number |
= |
{10}, |
| pages |
= |
{1568-1579}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=32189&arnumber=1498752&count=18&index=4}, |
| keyword |
= |
{} |
| } |
|
53 - Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model.
G. Poggi and G. Scarpa and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 43(8): pages 1901-1911, August 2005.
Keywords : Classification, Segmentation, Markov Fields.
@ARTICLE{ieeetgrs_05,
|
| author |
= |
{Poggi, G. and Scarpa, G. and Zerubia, J.}, |
| title |
= |
{Supervised Segmentation of Remote Sensing Images Based on a Tree-Structure MRF Model}, |
| year |
= |
{2005}, |
| month |
= |
{August}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{43}, |
| number |
= |
{8}, |
| pages |
= |
{1901-1911}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/32001/01487647.pdf?tp=&arnumber=1487647&isnumber=32001}, |
| keyword |
= |
{Classification, Segmentation, Markov Fields} |
| } |
|
54 - Dual Norms and Image Decomposition Models.
J.F. Aujol and A. Chambolle. International Journal of Computer Vision, 63(1): pages 85-104, June 2005.
Keywords : Image decomposition.
@ARTICLE{AujolChambolle,
|
| author |
= |
{Aujol, J.F. and Chambolle, A.}, |
| title |
= |
{Dual Norms and Image Decomposition Models}, |
| year |
= |
{2005}, |
| month |
= |
{June}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{63}, |
| number |
= |
{1}, |
| pages |
= |
{85-104}, |
| pdf |
= |
{http://www.springerlink.com/media/49te8d58yh6rtp9bfatn/contributions/r/3/4/5/r3454424r1106051.pdf}, |
| keyword |
= |
{Image decomposition} |
| } |
|
55 - Invariant Bayesian estimation on manifolds.
I. H. Jermyn. Annals of Statistics, 33(2): pages 583--605, April 2005.
Keywords : Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's.
@ARTICLE{jermyn_annstat05,
|
| author |
= |
{Jermyn, I. H.}, |
| title |
= |
{Invariant Bayesian estimation on manifolds}, |
| year |
= |
{2005}, |
| month |
= |
{April}, |
| journal |
= |
{Annals of Statistics}, |
| volume |
= |
{33}, |
| number |
= |
{2}, |
| pages |
= |
{583--605}, |
| url |
= |
{http://dx.doi.org/10.1214/009053604000001273}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_annstat05.pdf}, |
| keyword |
= |
{Bayesian estimation, MAP, MMSE, Invariant, Metric, Jeffrey's} |
| } |
Abstract :
A frequent and well-founded criticism of the maximum em a posteriori (MAP) and minimum mean squared error (MMSE) estimates of a continuous parameter param taking values in a differentiable manifold paramspace is that they are not invariant to arbitrary `reparametrizations' of paramspace. This paper clarifies the issues surrounding this problem, by pointing out the difference between coordinate invariance, which is a em sine qua non for a mathematically well-defined problem, and diffeomorphism invariance, which is a substantial issue, and then provides a solution. We first show that the presence of a metric structure on paramspace can be used to define coordinate-invariant MAP and MMSE estimates, and we argue that this is the natural way to proceed. We then discuss the choice of a metric structure on paramspace. By imposing an invariance criterion natural within a Bayesian framework, we show that this choice is essentially unique. It does not necessarily correspond to a choice of coordinates. In cases of complete prior ignorance, when Jeffreys' prior is used, the invariant MAP estimate reduces to the maximum likelihood estimate. The invariant MAP estimate coincides with the minimum message length (MML) estimate, but no discretization or approximation is used in its derivation. |
|
56 - Modeling very Oscillating Signals. Application to Image Processing.
G. Aubert and J.F. Aujol. Applied Mathematics and Optimization, 51(2): pages 163--182, March 2005.
@ARTICLE{AujolAubert,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Modeling very Oscillating Signals. Application to Image Processing}, |
| year |
= |
{2005}, |
| month |
= |
{March}, |
| journal |
= |
{Applied Mathematics and Optimization}, |
| volume |
= |
{51}, |
| number |
= |
{2}, |
| pages |
= |
{163--182}, |
| pdf |
= |
{http://www.springerlink.com/media/n9a4b9ftvj6jvlc2ux4g/contributions/5/t/5/4/5t543cht7hn2xchw.pdf}, |
| keyword |
= |
{} |
| } |
|
57 - Optimal Partitions, Regularized Solutions, and Application to Image Classification.
G. Aubert and J.F. Aujol. Applicable Analysis, 84(1): pages 15--35, January 2005.
@ARTICLE{AujolAubertclassif,
|
| author |
= |
{Aubert, G. and Aujol, J.F.}, |
| title |
= |
{Optimal Partitions, Regularized Solutions, and Application to Image Classification}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| journal |
= |
{Applicable Analysis}, |
| volume |
= |
{84}, |
| number |
= |
{1}, |
| pages |
= |
{15--35}, |
| keyword |
= |
{} |
| } |
|
58 - Image Decomposition into a Bounded Variation Component and an Oscillating Component.
J.F. Aujol and G. Aubert and L. Blanc-Féraud and A. Chambolle. Journal of Mathematical Imaging and Vision, 22(1): pages 71--88, January 2005.
@ARTICLE{BLA05,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L. and Chambolle, A.}, |
| title |
= |
{Image Decomposition into a Bounded Variation Component and an Oscillating Component}, |
| year |
= |
{2005}, |
| month |
= |
{January}, |
| journal |
= |
{Journal of Mathematical Imaging and Vision}, |
| volume |
= |
{22}, |
| number |
= |
{1}, |
| pages |
= |
{71--88}, |
| pdf |
= |
{http://springerlink.metapress.com/media/6n99d5dtvj6juld5tw5w/contributions/h/2/0/3/h20366rj1r34567m.pdf}, |
| keyword |
= |
{} |
| } |
|
59 - Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution.
F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Revue Française de Photogrammétrie et de Télédétection (SFPT), 180: pages 4--12, 2005.
Keywords : 3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images.
Copyright : SFPT
@ARTICLE{lafarge_sfpt05,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Modèle Paramétrique pour la Reconstruction Automatique en 3D de Zones Urbaines Denses à partir d'Images Satellitaires Haute Résolution}, |
| year |
= |
{2005}, |
| journal |
= |
{Revue Française de Photogrammétrie et de Télédétection (SFPT)}, |
| volume |
= |
{180}, |
| pages |
= |
{4--12}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2005_lafarge_sfpt05.pdf}, |
| keyword |
= |
{3D reconstruction, Urban areas, Bayesian approach, MCMC, Satellite images} |
| } |
|
60 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 108--125, September 2004.
Note : in Russian
@ARTICLE{DES04br,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
= |
{Applications of Gibbs fields methods to image processing problems}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| journal |
= |
{Problems of Information Transmission}, |
| volume |
= |
{40}, |
| number |
= |
{3}, |
| pages |
= |
{108--125}, |
| note |
= |
{in Russian}, |
| keyword |
= |
{} |
| } |
|
61 - Applications of Gibbs fields methods to image processing problems.
X. Descombes and E. Zhizhina. Problems of Information Transmission, 40(3): pages 279-295, September 2004.
Note : in English
@ARTICLE{DES04be,
|
| author |
= |
{Descombes, X. and Zhizhina, E.}, |
| title |
= |
{Applications of Gibbs fields methods to image processing problems}, |
| year |
= |
{2004}, |
| month |
= |
{September}, |
| journal |
= |
{Problems of Information Transmission}, |
| volume |
= |
{40}, |
| number |
= |
{3}, |
| pages |
= |
{279-295}, |
| note |
= |
{in English}, |
| keyword |
= |
{} |
| } |
|
62 - Modelling SAR Images with a Generalization of the Rayleigh Distribution.
E.E. Kuruoglu and J. Zerubia. IEEE Trans. Image Processing, 13(4): pages 527 - 533, April 2004.
@ARTICLE{Kuruoglu03,
|
| author |
= |
{Kuruoglu, E.E. and Zerubia, J.}, |
| title |
= |
{Modelling SAR Images with a Generalization of the Rayleigh Distribution}, |
| year |
= |
{2004}, |
| month |
= |
{April}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pages |
= |
{527 - 533}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284389.pdf?tp=&arnumber=1284389&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
63 - An object based approach for detecting smallbrain lesions: application to Virchow-Robin spaces.
X. Descombes and F. Kruggel and G. Wollny and H.J. Gertz. IEEE Trans. Medical Imaging, 23(2): pages 246--255, February 2004.
@ARTICLE{DES04a,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Wollny, G. and Gertz, H.J.}, |
| title |
= |
{An object based approach for detecting smallbrain lesions: application to Virchow-Robin spaces}, |
| year |
= |
{2004}, |
| month |
= |
{February}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{23}, |
| number |
= |
{2}, |
| pages |
= |
{246--255}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/42/28264/01263613.pdf?tp=&arnumber=1263613&isnumber=28264}, |
| keyword |
= |
{} |
| } |
|
64 - A Gibbs point process for road extraction in remotely sensed images.
R. Stoica and X. Descombes and J. Zerubia. International Journal of Computer Vision, 57(2): pages 121--136, 2004.
@ARTICLE{STO04a,
|
| author |
= |
{Stoica, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{A Gibbs point process for road extraction in remotely sensed images}, |
| year |
= |
{2004}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{57}, |
| number |
= |
{2}, |
| pages |
= |
{121--136}, |
| url |
= |
{http://www.springerlink.com/content/kr262t6084464n30/}, |
| keyword |
= |
{} |
| } |
|
65 - An adaptive Gaussian model for satellite image deblurring.
A. Jalobeanu and L. Blanc-Féraud and J. Zerubia. IEEE Trans. Image Processing, 13(4), 2004.
@ARTICLE{JAL04a,
|
| author |
= |
{Jalobeanu, A. and Blanc-Féraud, L. and Zerubia, J.}, |
| title |
= |
{An adaptive Gaussian model for satellite image deblurring}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{13}, |
| number |
= |
{4}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28667/01284396.pdf?tp=&arnumber=1284396&isnumber=28667}, |
| keyword |
= |
{} |
| } |
|
66 - Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification.
G. Rellier and X. Descombes and F. Falzon and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 42(7): pages 1543--1551, 2004.
@ARTICLE{DES04c,
|
| author |
= |
{Rellier, G. and Descombes, X. and Falzon, F. and Zerubia, J.}, |
| title |
= |
{Texture Feature Analysis Using a Gauss-Markov Model in Hyperspectral Image Classification}, |
| year |
= |
{2004}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{42}, |
| number |
= |
{7}, |
| pages |
= |
{1543--1551}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/36/29162/01315838.pdf?tp=&arnumber=1315838&isnumber=29162}, |
| keyword |
= |
{} |
| } |
|
67 - Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux.
M. Ortner and X. Descombes and J. Zerubia. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 173-174: pages 83--92, 2004.
@ARTICLE{ORT04a,
|
| author |
= |
{Ortner, M. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Extraction automatique de caricatures de bâtiments a partir de modeles numeriques d'elevation par utilisation de processus ponctuels spatiaux}, |
| year |
= |
{2004}, |
| journal |
= |
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, |
| volume |
= |
{173-174}, |
| pages |
= |
{83--92}, |
| keyword |
= |
{} |
| } |
|
68 - Gamma-convergence of discrete functionals with nonconvex perturbation for image classification.
G. Aubert and L. Blanc-Féraud and R. March. SIAM Journal on Numerical Analysis, 12(3): pages 1128--1145, 2004.
@ARTICLE{BLA04,
|
| author |
= |
{Aubert, G. and Blanc-Féraud, L. and March, R.}, |
| title |
= |
{Gamma-convergence of discrete functionals with nonconvex perturbation for image classification}, |
| year |
= |
{2004}, |
| journal |
= |
{SIAM Journal on Numerical Analysis}, |
| volume |
= |
{12}, |
| number |
= |
{3}, |
| pages |
= |
{1128--1145}, |
| keyword |
= |
{} |
| } |
|
69 - A nonlinear entropic variational model for image filtering.
A. Ben Hamza and H. Krim and J. Zerubia. EURASIP Journal on Applied Signal Processing, 16: pages 2408--2422, 2004.
@ARTICLE{JZHK04,
|
| author |
= |
{Ben Hamza, A. and Krim, H. and Zerubia, J.}, |
| title |
= |
{A nonlinear entropic variational model for image filtering}, |
| year |
= |
{2004}, |
| journal |
= |
{EURASIP Journal on Applied Signal Processing}, |
| volume |
= |
{16}, |
| pages |
= |
{2408--2422}, |
| keyword |
= |
{} |
| } |
|
70 - A Multiresolution Approach for Shape from Shading Coupling Deterministic and Stochastic Optimization.
A. Crouzil and X. Descombes and J.D. Durou. IEEE Trans. Pattern Analysis ans Machine Intelligence, 25(11): pages 1416--1421, November 2003.
Note : Special section on `Energy minimization methods in computer vision and pattern recognition'
@ARTICLE{crouzilXDJDD,
|
| author |
= |
{Crouzil, A. and Descombes, X. and Durou, J.D.}, |
| title |
= |
{A Multiresolution Approach for Shape from Shading Coupling Deterministic and Stochastic Optimization}, |
| year |
= |
{2003}, |
| month |
= |
{November}, |
| journal |
= |
{IEEE Trans. Pattern Analysis ans Machine Intelligence}, |
| volume |
= |
{25}, |
| number |
= |
{11}, |
| pages |
= |
{1416--1421}, |
| note |
= |
{Special section on `Energy minimization methods in computer vision and pattern recognition'}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/34/27807/01240116.pdf?tp=&arnumber=1240116&isnumber=27807}, |
| keyword |
= |
{} |
| } |
|
71 - Wavelet-based Level Set Evolution for Classification of Textured Images.
J.F. Aujol and G. Aubert and L. Blanc-Féraud. IEEE Trans. Image Processing, 12(12), 2003.
@ARTICLE{aujolGL,
|
| author |
= |
{Aujol, J.F. and Aubert, G. and Blanc-Féraud, L.}, |
| title |
= |
{Wavelet-based Level Set Evolution for Classification of Textured Images}, |
| year |
= |
{2003}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{12}, |
| number |
= |
{12}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/83/28122/01257399.pdf?tp=&arnumber=1257399&isnumber=28122}, |
| keyword |
= |
{} |
| } |
|
72 - 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}, |
| keyword |
= |
{} |
| } |
|
73 - Droplet Shapes for a Class of Models in Z^2 at Zero Temperature.
X. Descombes and E. Pechersky. Journal of Statistical Physics, 111(1-2): pages 129--169, 2003.
@ARTICLE{descombesEP,
|
| author |
= |
{Descombes, X. and Pechersky, E.}, |
| title |
= |
{Droplet Shapes for a Class of Models in Z^2 at Zero Temperature}, |
| year |
= |
{2003}, |
| journal |
= |
{Journal of Statistical Physics}, |
| volume |
= |
{111}, |
| number |
= |
{1-2}, |
| pages |
= |
{129--169}, |
| pdf |
= |
{http://www.springerlink.com/media/f62clcyryp7vykvrfl1y/contributions/m/1/5/3/m15314jr382r593n.pdf}, |
| keyword |
= |
{} |
| } |
|
74 - 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}, |
| keyword |
= |
{} |
| } |
|
75 - Satellite image debluring 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 debluring using complex wavelet packets}, |
| year |
= |
{2003}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{51}, |
| number |
= |
{3}, |
| pages |
= |
{205--217}, |
| pdf |
= |
{http://www.springerlink.com/media/788y661nyh6vwm80hqrl/contributions/t/2/6/0/t26074p520211l84.pdf}, |
| keyword |
= |
{} |
| } |
|
76 - Skewed alpha-stable distributions for modelling textures.
E.E. Kuruoglu and J. Zerubia. Pattern Recognition Letters, 24: 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}, |
| pages |
= |
{339--348}, |
| keyword |
= |
{} |
| } |
|
77 - 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 |
= |
{} |
| } |
|
78 - 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 |
= |
{} |
| } |
|
79 - 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}, |
| keyword |
= |
{} |
| } |
|
80 - 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}, |
| keyword |
= |
{} |
| } |
|
81 - Globally optimal regions and boundaries as minimum ratio weight cycles.
I. H. Jermyn and H. Ishikawa. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(10): pages 1075-1088, October 2001.
Keywords : Graph, Ratio, Cycle, Segmentation, Global minimum.
Copyright : ©2008 IEEE. 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.
@ARTICLE{jermyn_tpami01,
|
| author |
= |
{Jermyn, I. H. and Ishikawa, H.}, |
| title |
= |
{Globally optimal regions and boundaries as minimum ratio weight cycles}, |
| year |
= |
{2001}, |
| month |
= |
{October}, |
| journal |
= |
{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
| volume |
= |
{23}, |
| number |
= |
{10}, |
| pages |
= |
{1075-1088}, |
| url |
= |
{http://dx.doi.org/10.1109/34.954599}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/jermyn_tpami01.pdf}, |
| keyword |
= |
{Graph, Ratio, Cycle, Segmentation, Global minimum} |
| } |
Abstract :
We describe a new form of energy functional for the modelling and identification of regions in images. The energy is defined on the space of boundaries in the image domain, and can incorporate very general combinations of modelling information both from the boundary (intensity gradients,ldots), em and from the interior of the region (texture, homogeneity,ldots). We describe two polynomial-time digraph algorithms for finding the em global minima of this energy. One of the algorithms is completely general, minimizing the functional for any choice of modelling information. It runs in a few seconds on a 256 times 256 image. The other algorithm applies to a subclass of functionals, but has the advantage of being extremely parallelizable. Neither algorithm requires initialization. |
|
82 - 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}, |
| keyword |
= |
{} |
| } |
|
83 - 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. |
|
84 - 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}, |
| keyword |
= |
{} |
| } |
|
85 - 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}, |
| keyword |
= |
{} |
| } |
|
86 - 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}, |
| keyword |
= |
{} |
| } |
|
87 - 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 |
= |
{} |
| } |
|
88 - Comparison of Filtering Methods for fMRI Datasets.
F. Kruggel and Y. Von Cramon and X. Descombes. NeuroImage, 10: pages 530-543, November 1999.
@ARTICLE{xd99d,
|
| author |
= |
{Kruggel, F. and Von Cramon, Y. and Descombes, X.}, |
| title |
= |
{Comparison of Filtering Methods for fMRI Datasets}, |
| year |
= |
{1999}, |
| month |
= |
{November}, |
| journal |
= |
{NeuroImage}, |
| volume |
= |
{10}, |
| pages |
= |
{530-543}, |
| keyword |
= |
{} |
| } |
|
89 - Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours.
L. Blanc-Féraud and G. Aubert. International Journal of Computer Vision, 34(1): pages 19-28, September 1999.
@ARTICLE{lbf99a,
|
| author |
= |
{Blanc-Féraud, L. and Aubert, G.}, |
| title |
= |
{Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours}, |
| year |
= |
{1999}, |
| month |
= |
{September}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{34}, |
| number |
= |
{1}, |
| pages |
= |
{19-28}, |
| keyword |
= |
{} |
| } |
|
90 - 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. |
|
91 - A Markov Pixon Information approach for low level image description.
X. Descombes and F. Kruggel. IEEE Trans. Pattern Analysis ans Machine Intelligence, 21(6): pages 482-494, June 1999.
@ARTICLE{xd99b,
|
| author |
= |
{Descombes, X. and Kruggel, F.}, |
| title |
= |
{A Markov Pixon Information approach for low level image description}, |
| year |
= |
{1999}, |
| month |
= |
{June}, |
| journal |
= |
{IEEE Trans. Pattern Analysis ans Machine Intelligence}, |
| volume |
= |
{21}, |
| number |
= |
{6}, |
| pages |
= |
{482-494}, |
| keyword |
= |
{} |
| } |
|
92 - Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline.
T. Corbard and L. Blanc-Féraud and G. Berthomieu and J. Provost. Astronomy and Astrophysics, (344): pages 696-708, 1999.
@ARTICLE{lbf99b,
|
| author |
= |
{Corbard, T. and Blanc-Féraud, L. and Berthomieu, G. and Provost, J.}, |
| title |
= |
{Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline}, |
| year |
= |
{1999}, |
| journal |
= |
{Astronomy and Astrophysics}, |
| number |
= |
{344}, |
| pages |
= |
{696-708}, |
| keyword |
= |
{} |
| } |
|
93 - GMRF Parameter Estimation in a non-stationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging.
X. Descombes and M. Sigelle and F. Prêteux. IEEE Trans. Image Processing, 8(4): pages 490-503, 1999.
@ARTICLE{xd99a,
|
| author |
= |
{Descombes, X. and Sigelle, M. and Prêteux, F.}, |
| title |
= |
{GMRF Parameter Estimation in a non-stationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging}, |
| year |
= |
{1999}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{8}, |
| number |
= |
{4}, |
| pages |
= |
{490-503}, |
| keyword |
= |
{} |
| } |
|
94 - 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. |
|
95 - Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm.
A. Strandlie and J. Zerubia. Computer Physics Communications, (123): 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}, |
| number |
= |
{123}, |
| pages |
= |
{77-87}, |
| keyword |
= |
{} |
| } |
|
96 - Variational approach for edge preserving regularization using coupled PDE's.
S. Teboul and L. Blanc-Féraud and G. Aubert and M. Barlaud. IEEE Trans. Image Processing, 7(3): pages 387-397, March 1998.
@ARTICLE{lbf98,
|
| author |
= |
{Teboul, S. and Blanc-Féraud, L. and Aubert, G. and Barlaud, M.}, |
| title |
= |
{Variational approach for edge preserving regularization using coupled PDE's}, |
| year |
= |
{1998}, |
| month |
= |
{March}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{7}, |
| number |
= |
{3}, |
| pages |
= |
{387-397}, |
| keyword |
= |
{} |
| } |
|
97 - Combined constraints for efficient algebraic regularized methods.
I. Laurette and J. Darcourt and L. Blanc-Féraud and P.M. Koulibaly and M. Barlaud. Physics in Medicine and Biology, 34(4): pages 991-1000, 1998.
@ARTICLE{lbf98a,
|
| author |
= |
{Laurette, I. and Darcourt, J. and Blanc-Féraud, L. and Koulibaly, P.M. and Barlaud, M.}, |
| title |
= |
{Combined constraints for efficient algebraic regularized methods}, |
| year |
= |
{1998}, |
| journal |
= |
{Physics in Medicine and Biology}, |
| volume |
= |
{34}, |
| number |
= |
{4}, |
| pages |
= |
{991-1000}, |
| keyword |
= |
{} |
| } |
|
98 - A generalized sampling theory without bandlimiting constraints.
M. Unser and J. Zerubia. IEEE Trans. on Circuits And Systems II, 45(8): pages 959-969, 1998.
@ARTICLE{jz98b,
|
| author |
= |
{Unser, M. and Zerubia, J.}, |
| title |
= |
{A generalized sampling theory without bandlimiting constraints}, |
| year |
= |
{1998}, |
| journal |
= |
{IEEE Trans. on Circuits And Systems II}, |
| volume |
= |
{45}, |
| number |
= |
{8}, |
| pages |
= |
{959-969}, |
| keyword |
= |
{} |
| } |
|
99 - fMRI Signal Restoration Using an Edge Preserving Spatio-temporal Markov Random Field.
X. Descombes and F. Kruggel and Y. von Cramon. NeuroImage, 8: pages 340-349, 1998.
Keywords : fMRI, Restoration, Markov Fields.
Copyright : published in NeuroIMage by Elsevier
||http://www.elsevier.com/wps/find/homepage.cws_home
@ARTICLE{descombes98d,
|
| author |
= |
{Descombes, X. and Kruggel, F. and von Cramon, Y.}, |
| title |
= |
{fMRI Signal Restoration Using an Edge Preserving Spatio-temporal Markov Random Field}, |
| year |
= |
{1998}, |
| journal |
= |
{NeuroImage}, |
| volume |
= |
{8}, |
| pages |
= |
{340-349}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/1998_descombes98d.pdf}, |
| keyword |
= |
{fMRI, Restoration, Markov Fields} |
| } |
|
100 - Spatio-temporal fMRI analysis using Markov Random Fields.
X. Descombes and F. Kruggel and Y. Von Cramon. IEEE Trans. Medical Imaging, 17: pages 1028-1039, 1998.
Note : to appear.
Keywords : fMRI, Markov Random Fields.
@ARTICLE{descombes98,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Von Cramon, Y.}, |
| title |
= |
{Spatio-temporal fMRI analysis using Markov Random Fields}, |
| year |
= |
{1998}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{17}, |
| pages |
= |
{1028-1039}, |
| keyword |
= |
{fMRI, Markov Random Fields} |
| } |
|
 top of the page
These pages were generated by 
|
get the bibtex
