P. Craciun and J. Zerubia, Towards efficient simulation of marked point process models for boat extraction from high resolution optical remotely sensed images, 2014 IEEE Geoscience and Remote Sensing Symposium, 2014.
DOI : 10.1109/IGARSS.2014.6946929

URL : https://hal.archives-ouvertes.fr/hal-01016874

N. Cressie and C. Wikle, Statistics for spatio-temporal data. Wiley series in probabilities and statistics, 2011.

P. Cr?-aciun and J. Zerubia, Unsupervised marked point process model for boat extraction in harbors from high resolution optical remotely sensed image, Proc. ICIP, pp.4122-4125, 2013.

N. Dalai and B. Triggs, Histograms of oriented gradients for human detection, Proc. CVPR, pp.886-893, 2005.

F. De-chaumont, Icy: an open bioimage informatics platform for extended reproducible research, Nature Methods, vol.9, issue.7, pp.690-696, 2012.
DOI : 10.1038/nmeth.2075

X. Descombes, F. Chatelain, F. Lafarge, C. Lantuejoul, C. Mallet et al., Stochastic Geometry for Image Analysis, 2011.
DOI : 10.1002/9781118601235

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

P. Diggle, Spatio-temporal point processes: methods and applications. Statistical methods for spatiotemporal systems, pp.1-45, 2007.
DOI : 10.1201/9781420011050.ch1

URL : javascript:void(0)

P. Diggle, B. Rowlingson, and T. Su, Point process methodology for on-line spatio-temporal disease surveillance, Environmetrics, vol.60, issue.5, pp.423-434, 2005.
DOI : 10.1002/env.712

W. Ge and R. T. Collins, Marked point processes for crowd counting, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.2913-2920, 2009.
DOI : 10.1109/CVPR.2009.5206621

F. Goudail, P. Réfrégier, and G. Delyon, Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images, Journal of the Optical Society of America A, vol.21, issue.7, pp.1231-1240, 2004.
DOI : 10.1364/JOSAA.21.001231

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

E. Jensen, K. Jónsdóttir, J. Schmiegel, and O. Barndoff-nielsen, Spatio-temporal modeling -with a view to biological growth. Statistical methods for spatio-temporal systems, pp.47-75, 2007.

B. Leibe, K. Schindler, N. Cornelis, and L. Van-gool, Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.10, pp.1683-1698, 2008.
DOI : 10.1109/TPAMI.2008.170

S. Oh, S. Russell, and S. Sastry, Markov Chain Monte Carlo Data Association for general multipletarget tracking problems, Proc. CDC, vol.1, pp.735-742, 2004.

R. Peng, F. Schoenberg, and J. Woods, A Space???Time Conditional Intensity Model for Evaluating a Wildfire Hazard Index, Journal of the American Statistical Association, vol.100, issue.469, pp.26-35, 2005.
DOI : 10.1198/016214504000001763

K. Smith, V. Carleton, and . Lepetit, General constraints for batch Multiple-Target Tracking applied to large-scale videomicroscopy, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587506

M. Van-lieshout, Markov Point Processes and Their Applications, 2000.
DOI : 10.1142/p060

Y. Verdié and F. Lafarge, Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes, Proc. ECCV, pp.539-552, 2012.
DOI : 10.1007/978-3-642-33712-3_39

G. Welch and G. Bishop, An introduction to the Kalman filter, Proc. SIGGRAPH, pp.19-24, 2001.

Q. Yu and G. Medioni, Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.312196-2210, 2009.
DOI : 10.1109/CVPR.2007.382991