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

J. E. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, 1986.
DOI : 10.1016/0031-3203(83)90012-2

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, PAMI, vol.23, issue.11, 2001.
DOI : 10.1109/iccv.1999.791245

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

J. Byrd, S. Jarvis, and A. Bhalerao, On the parallelisation of MCMC-based image processing, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010.
DOI : 10.1109/IPDPSW.2010.5470896

D. Chai, W. Forstner, and F. Lafarge, Recovering Line-Networks in Images by Junction-Point Processes, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.247

URL : https://hal.archives-ouvertes.fr/hal-00814262

D. Chai, W. Forstner, and M. Y. Yang, COMBINE MARKOV RANDOM FIELDS AND MARKED POINT PROCESSES TO EXTRACT BUILDING FROM REMOTELY SENSED IMAGES, ISPRS congress, 2012.
DOI : 10.5194/isprsannals-I-3-365-2012

X. Descombes, Stochastic Geometry for Image Analysis, 2011.
DOI : 10.1002/9781118601235

URL : https://hal.archives-ouvertes.fr/hal-00793677

X. Descombes, R. Minlos, and E. Zhizhina, Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum, Journal of Mathematical Imaging and Vision, vol.21, issue.3, 2009.
DOI : 10.1007/s10851-008-0117-y

URL : https://hal.archives-ouvertes.fr/inria-00422411

D. Earl and M. Deem, Parallel tempering: Theory, applications, and new perspectives, Physical Chemistry Chemical Physics, vol.96, issue.23, 2005.
DOI : 10.1002/cphc.200400629

URL : http://arxiv.org/abs/physics/0508111

W. Ge and R. Collins, Marked point processes for crowd counting, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206621

J. Gonzalez, Y. Low, A. Gretton, and C. Guestrin, Parallel Gibbs sampling: From colored fields to thin junction trees, Journal of Machine Learning Re- search, 2011.

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

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

F. Han, Z. W. Tu, and S. Zhu, Range image segmentation by an effective jump-diffusion method, PAMI, vol.26, issue.9, 2004.

M. Harkness and P. Green, Parallel chains, delayed rejection and reversible jump mcmc for object recognition, 2000.

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

C. Lacoste, X. Descombe, 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, 2005.
DOI : 10.1109/TPAMI.2005.206

F. Lafarge, G. Gimel-'farb, and X. Descombes, Geometric feature extraction by a multi-marked point process, PAMI, vol.32, issue.9, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00503140

F. Lafarge and C. Mallet, Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation, International Journal of Computer Vision, vol.47, issue.2, 2012.
DOI : 10.1007/s11263-012-0517-8

URL : https://hal.archives-ouvertes.fr/hal-00759265

A. Lehmussola, P. Ruusuvuori, J. Selinummi, H. Huttunen, and O. Yli-harja, Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations, IEEE Transactions on Medical Imaging, vol.26, issue.7, 2007.
DOI : 10.1109/TMI.2007.896925

V. Lempitsky and A. Zisserman, Learning to count objects in images, 2010.

S. Li, Markov Random Field Modeling in Image Analysis, 2001.
DOI : 10.1007/978-4-431-67044-5

M. V. Lieshout, Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.7, 2008.
DOI : 10.1109/TPAMI.2008.45

J. Liu, Monte Carlo Strategies in Scientific Computing, 2001.
DOI : 10.1007/978-0-387-76371-2

C. Mallet, F. Lafarge, M. Roux, U. Soergel, F. Bretar et al., A marked point process for modeling lidar waveforms, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00503149

H. Nguyen, R. Fablet, and J. Bouchet, Spatial Statistics of Visual Keypoints for Texture Recognition, 2010.
DOI : 10.1007/978-3-642-15561-1_55

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, 2008.
DOI : 10.1109/TPAMI.2007.1159

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

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

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

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, 2002.
DOI : 10.1016/S0378-3758(01)00195-1

R. S. Stoica, V. Martinez, and E. Saar, A three-dimensional object point process for detection of cosmic filaments, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.434, issue.4, 2007.
DOI : 10.1111/j.1467-9876.2007.00587.x

K. Sun, N. Sang, and T. Zhang, Marked point process for vasculartree extraction on angiogram, 2007.

R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov et al., Comparative study of energy minimization methods for markov random fields with smoothnessbased priors, PAMI, vol.30, issue.6, 2008.

Z. Tu and S. Zhu, Image Segmentation by Data- Driven Markov Chain Monte Carlo, PAMI, vol.24, issue.5, 2002.

A. Utasi and C. Benedek, A 3-D marked point process model for multi-view people detection, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995699

Y. Verdie and F. Lafarge, Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes, 2012.
DOI : 10.1007/978-3-642-33712-3_39

URL : https://hal.archives-ouvertes.fr/hal-00742770

Y. Weiss and W. Freeman, On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs, IEEE Transactions on Information Theory, vol.47, issue.2, 2001.
DOI : 10.1109/18.910585