G. Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction, 1995.
DOI : 10.1007/978-3-642-55760-6

S. Li, Markov Random Field Modeling in Computer Vision, 1995.
DOI : 10.1007/978-4-431-66933-3

W. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

P. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

A. Dick, P. Torr, and R. Cipolla, Modelling and Interpretation of Architecture from Several Images, International Journal of Computer Vision, vol.60, issue.2, pp.111-134, 2004.
DOI : 10.1023/B:VISI.0000029665.07652.61

F. Lafarge, X. Descombes, J. Zerubia, and M. Pierrot-deseilligny, Building reconstruction from a single DEM, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587778

URL : https://hal.archives-ouvertes.fr/hal-00781733

Y. Wu, S. Zhu, and C. Guo, Statistical Modeling of Texture Sketch, European Conference Computer Vision, 2002.
DOI : 10.1007/3-540-47977-5_16

C. E. Guo, S. Zhu, and Y. Wu, Modeling visual patterns by integrating descriptive and generative models, International Journal of Computer Vision, vol.53, issue.1, pp.5-29, 2003.
DOI : 10.1023/A:1023023207396

G. Perrin, X. Descombes, and J. Zerubia, Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application, Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005.
DOI : 10.1007/11585978_1

URL : https://hal.archives-ouvertes.fr/hal-00113382

M. Ortner, X. Descombes, and J. Zerubia, Building Outline Extraction from Digital Elevation Models Using Marked Point Processes, International Journal of Computer Vision, vol.24, issue.5, pp.107-132, 2007.
DOI : 10.1007/s11263-005-5033-7

C. Lacoste, X. Descombes, and J. Zerubia, Point processes for unsupervised line network extraction in remote sensing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1568-1579, 2005.
DOI : 10.1109/TPAMI.2005.206

O. Tournaire, N. Paparoditis, and F. Lafarge, Rectangular road marking detection with marked point processes, Proc. conference on Photogrammetric Image Analysis, 2007.

K. Sun, N. Sang, and T. Zhang, Marked Point Process for Vascular Tree Extraction on Angiogram, Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007.
DOI : 10.1007/978-3-540-74198-5_36

S. Descamps, X. Descombes, A. Béchet, and J. Zerubia, Automatic Flamingo detection using a multiple birth and death process, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
DOI : 10.1109/ICASSP.2008.4517809

M. Van-lieshout, Markov point processes and their applications, 2000.
DOI : 10.1142/p060

C. Geyer and J. Moller, Simulation and likelihood inference for spatial point processes, Scandinavian Journal of Statistics, issue.21, pp.359-373, 1994.

M. Metropolis, A. Rosenbluth, A. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1092, 1953.
DOI : 10.1063/1.1699114

S. Geman and C. Huang, Diffusions for Global Optimization, SIAM Journal on Control and Optimization, vol.24, issue.5, pp.131-143, 1986.
DOI : 10.1137/0324060

U. Grenander and M. Miller, Representations of Knowledge in Complex Systems, Journal of the Royal Statistical Society, vol.56, issue.4, pp.1-33, 1994.

F. Lafarge and G. Gimel-'farb, Texture Representation by Geometric Objects using a Jump-Diffusion Process, Procedings of the British Machine Vision Conference 2008, 2008.
DOI : 10.5244/C.22.114

URL : https://hal.archives-ouvertes.fr/hal-00781762

A. Baddeley and M. Van-lieshout, Stochastic geometry models in high-level vision, Journal of Applied Statistics, vol.55, issue.5-6, pp.233-258, 1993.
DOI : 10.1098/rsta.1990.0127

H. Rue and A. Syverseen, Bayesian object recognition with baddeley's delta loss, Advances in Applied Probability, vol.LVI, issue.01, pp.64-84, 1998.
DOI : 10.1093/biomet/57.1.97

A. Pievatolo and P. Green, Boundary detection through dynamic polygons, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.60, issue.3, pp.609-626, 1998.
DOI : 10.1111/1467-9868.00143

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.6, issue.6, pp.721-741, 1984.

M. Ortner, X. Descombes, and J. Zerubia, A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.1, pp.105-119, 2008.
DOI : 10.1109/TPAMI.2007.1159

URL : https://hal.archives-ouvertes.fr/inria-00278882

T. Wu, G. Xia, and S. Zhu, Compositional Boosting for Computing Hierarchical Image Structures, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383034

A. Desolneux, L. Moisan, and J. Morel, From Gestalt Theory to Image Analysis: A Probabilistic Approach, 2008.
DOI : 10.1007/978-0-387-74378-3

URL : https://hal.archives-ouvertes.fr/hal-00259077

A. Srivastava, M. Miller, and U. Grenander, Multiple target direction of arrival tracking, IEEE Transactions on Signal Processing, vol.43, issue.5, pp.282-285, 1995.
DOI : 10.1109/78.382418

A. Srivastava, U. Grenander, G. Jensen, and M. Miller, Jump???diffusion Markov processes on orthogonal groups for object pose estimation, Journal of Statistical Planning and Inference, vol.103, issue.1-2, pp.15-37, 2002.
DOI : 10.1016/S0378-3758(01)00195-1

F. Han, Z. Tu, and S. Zhu, Range Image Segmentation by an Effective Jump-Diffusion Method, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1138-1153, 2004.

P. Van-laarhoven and E. Aarts, Simulated Annealing : Theory and Applications, 1987.
DOI : 10.1007/978-94-015-7744-1

J. Varanelli, On the aacceleration of the simulated annealing, 1996.

H. Haario and E. Saksman, Simulated annealing process in general state space, Advances in Applied Probability, issue.23, pp.866-893, 1991.

S. White, Concepts of scale in simulated annealing, AIP Conference Proceedings, 1984.
DOI : 10.1063/1.34823

M. Rochery, I. Jermyn, and J. Zerubia, Higher Order Active Contours, International Journal of Computer Vision, vol.24, issue.12, pp.27-42, 2006.
DOI : 10.1007/s11263-006-6851-y

URL : https://hal.archives-ouvertes.fr/inria-00070352

M. Chantler, M. Petrou, A. Penirsche, M. Schmidt, and G. Mcgunnigle, Classifying Surface Texture while Simultaneously Estimating Illumination Direction, International Journal of Computer Vision, vol.62, issue.1-2, pp.83-96, 2005.
DOI : 10.1007/s11263-005-4636-3

M. Varma and A. Zisserman, A Statistical Approach to Texture Classification from Single Images, International Journal of Computer Vision, vol.62, issue.1-2, pp.61-81, 2005.
DOI : 10.1007/s11263-005-4635-4

S. Lazebnik, C. Schmid, and J. Ponce, A sparse texture representation using local affine regions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1265-1278, 2005.
DOI : 10.1109/TPAMI.2005.151

URL : https://hal.archives-ouvertes.fr/inria-00548530

J. Portilla and E. Simoncelli, A parametric texture model based on joint statistics of complex wavelet coefficients, International Journal of Computer Vision, vol.40, issue.1, pp.49-71, 2000.
DOI : 10.1023/A:1026553619983

B. Julesz, S. Zhu, C. E. Guo, Y. Wang, and Z. Xu, Textons, the elements of texture perception and their interactions What are textons?, Nature International Journal of Computer Vision, vol.290, issue.62 12, pp.91-97, 1981.

J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.8, issue.6, pp.679-698, 1986.