D. Ahirwal, A. Hébraud, R. Kádár, M. Wilhelm, and G. Schlatter, From self-assembly of electrospun nanofibers to 3D cm thick hierarchical foams, Soft Matter, vol.6, issue.11, pp.3164-3172, 2013.
DOI : 10.1039/c2sm27543k

F. Al-awadhi, C. Jennison, and M. Hurn, Statistical image analysis for a confocal microscopy two-dimensional section of cartilage growth, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.181, issue.1, pp.31-49, 2004.
DOI : 10.1214/aoap/1027961031

A. Baddeley and M. N. Van-lieshout, Stochastic geometry models in high-level vision, Statistics and Images: 1 (Eds K.V. Mardia and G.K. Kanji), pp.2335-256, 1993.
DOI : 10.1098/rsta.1990.0127

S. P. Brooks, P. Giudici, and G. O. Roberts, Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.28, issue.1, pp.3-55, 2003.
DOI : 10.1016/S0165-1684(00)00192-4

S. Eap, A. Ferrand, C. M. Palomares, A. Hébraud, J. F. Stoltz et al., Electrospun nanofibrous 3D scaffold for bone tissue engineering, Bio-Medical Materials and Engineering, vol.22, pp.137-141, 2012.

P. J. 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

M. Imberty and X. Descombes, Simulation de processus objets : Etude de faisabilité pour une applicationàapplication`applicationà la segmentation d'image, 2000.

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

B. Konomi, S. Dahval, J. Huang, S. Kundu, D. Huitink et al., Bayesian object classification of gold nanoparticles, The Annals of Applied Statistics, vol.7, issue.2, pp.640-668, 2013.
DOI : 10.1214/12-AOAS616SUPP

C. Lacoste, X. Descombes, and J. Zerubia, A Comparative Study of Point Processes for Line Network Extraction in Remote Sensing, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00072072

C. Lacoste, X. Descombes, and J. Zerubia, Road network extraction in remote sensing by a Markov object process, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003.
DOI : 10.1109/ICIP.2003.1247420

N. Lavielle, A. Hébraud, C. Mendoza, A. Ferrand, N. Benkirane-jessel et al., Structuring and Molding of Electrospun Nanofibers: Effect of Electrical and Topographical Local Properties of Micro-Patterned Collectors, Macromolecular Materials and Engineering, vol.7, issue.10, pp.958-968, 2012.
DOI : 10.1002/mame.201100327

N. Lavielle, M. De-geus, A. Hébraud, G. Schlatter, R. Rossi et al., Controlled formation of poly(??-caprolactone) ultrathin electrospun nanofibers in a hydrolytic degradation-assisted process, European Polymer Journal, vol.49, issue.6, pp.1331-1336, 2013.
DOI : 10.1016/j.eurpolymj.2013.02.038

J. Møller and R. P. Waagepetersen, Statistical Inference and Simulation for Spatial Point Processes, 2004.
DOI : 10.1201/9780203496930

G. Perrin, X. Descombes, and J. Zerubia, A Non-Bayesian Model for Tree Crown Extraction using Marked Point Processes, p.5846, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00070180

E. H. Shin, K. S. Cho, M. H. Seo, and H. Kim, Determination of electrospun fiber diameter distributions using image analysis processing, Macromolecular Research, vol.23, issue.4, pp.314-319, 2008.
DOI : 10.1007/BF03218523

R. Stoica, Marked point processes for statistical and morphological analysis of astronomical data, The European Physical Journal Special Topics, vol.24, issue.1, pp.123-165, 2010.
DOI : 10.1140/epjst/e2010-01262-7

Z. Tu and S. Zhu, Image segmentation by data-driven Markov Chain Monte Carlo, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp.657-673, 2002.

M. N. Van-lieshout, Markov point processes and their applications in high-level imaging, Bulletin of the International Statistical Institute LVI, pp.559-576, 1995.

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

V. Vigneron, T. Q. Syed, G. Barlovatz-meimon, M. Malo, C. Montagne et al., Adaptive filtering and hypothesis testing: Application to cancerous cells detection, Pattern Recognition Letters, vol.31, issue.14, pp.2214-2224, 2010.
DOI : 10.1016/j.patrec.2010.05.023
URL : https://hal.archives-ouvertes.fr/hal-00744931