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The Transparent Dead Leaves Model

Published online by Cambridge University Press:  04 January 2016

B. Galerne*
Affiliation:
ENS Cachan
Y. Gousseau*
Affiliation:
Télécom ParisTech
*
Postal address: CMLA, ENS Cachan, CNRS, UniverSud, 61 Avenue du Président Wilson, F-94230 Cachan, France. Email address: [email protected]
∗∗ Postal address: LTCI CNRS, Télécom ParisTech, 46 rue Barrault, F-75013 Paris, France.
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Abstract

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In this paper we introduce the transparent dead leaves (TDL) random field, a new germ-grain model in which the grains are combined according to a transparency principle. Informally, this model may be seen as the superposition of infinitely many semitransparent objects. It is therefore of interest in view of the modeling of natural images. Properties of this new model are established and a simulation algorithm is proposed. The main contribution of the paper is to establish a central limit theorem, showing that, when varying the transparency of the grain from opacity to total transparency, the TDL model ranges from the dead leaves model to a Gaussian random field.

Type
Stochastic Geometry and Statistical Applications
Copyright
© Applied Probability Trust 

References

Baddeley, A. (2007). Spatial point processes and their applications. In Stochastic Geometry (Lecture Notes Math. 1892), ed. Weil, W., Springer, Berlin, pp. 175.Google Scholar
Barral, J. and Mandelbrot, B. B. (2002). Multifractal products of cylindrical pulses. Prob. Theory Relat. Fields 124, 409430.Google Scholar
Baryshnikov, Y. and Yukich, J. E. (2005). Gaussian limits for random measures in geometric probability. Ann. Appl. Prob. 15, 213253.CrossRefGoogle Scholar
Bickel, P. J. and Doksum, K. A. (2001). Mathematical Statistics, Vol. I, 2nd edn. Prentice Hall.Google Scholar
Bordenave, C., Gousseau, Y. and Roueff, F. (2006). The dead leaves model: a general tessellation modeling occlusion. Adv. Appl. Prob. 38, 3146.CrossRefGoogle Scholar
Bovier, A. and Picco, P. (1993). A law of the iterated logarithm for random geometric series. Ann. Prob. 21, 168184.Google Scholar
Cao, F., Guichard, F. and Hornung, H. (2010). Dead leaves model for measuring texture quality on a digital camera. In Digital Photography VI (Proc. SPIE 7537), eds Imai, F. H., Sampat, N. and Xiao, F., 8pp.CrossRefGoogle Scholar
Chainais, P. (2007). Infinitely divisible cascades to model the statistics of natural images. IEEE Trans. Pattern Analysis Mach. Intellig. 29, 21052119.CrossRefGoogle ScholarPubMed
Enderton, E., Sintorn, E., Shirley, P. and Luebke, D. (2010). Stochastic transparency. In Proc. 2010 ACM SIGGRAPH Symp. Interactive 3D Graphics and Games, ACM, New York, pp. 157164.Google Scholar
Galerne, B., Gousseau, Y. and Morel, J.-M. (2011). Random phase textures: theory and synthesis. IEEE Trans. Image Process. 20, 257267.CrossRefGoogle ScholarPubMed
Gousseau, Y. and Roueff, F. (2007). Modeling occlusion and scaling in natural images. Multiscale Model. Simul. 6, 105134.Google Scholar
Grosjean, B. and Moisan, L. (2009). A-contrario detectability of spots in textured backgrounds. J. Math. Imaging Vision 33, 313337.Google Scholar
Heinrich, L. and Schmidt, V. (1985). Normal convergence of multidimensional shot noise and rates of this convergence. Adv. Appl. Prob. 17, 709730.CrossRefGoogle Scholar
Jeulin, D. (1997). Dead leaves models: from space tesselation to random functions. In Proc. Internat. Symp. Advances in Theory and Applications of Random Sets, ed. Jeulin, D., World Scientific, River Edge, NJ, pp. 137156.Google Scholar
Kendall, W. S. and Thönnes, E. (1999). Perfect simulation in stochastic geometry. Pattern Recognition 32, 15691586.CrossRefGoogle Scholar
Kingman, J. F. C. (1993). Poisson Processes (Oxford Stud. Prob. 3). Oxford University Press.Google Scholar
Lantuéjoul, C. (2002). Geostatistical Simulation: Models and Algorithms. Springer, Berlin.CrossRefGoogle Scholar
Matheron, G. (1968). Schéma booléen séquentiel de partition aléatoire. Tech. Rep. 89, CMM.Google Scholar
Matheron, G. (1975). Random Sets and Integral Geometry. John Wiley, New York.Google Scholar
Michalowicz, J., Nichols, J. M., Bucholtz, F. and Olson, C. C. (2009). An Isserlis' theorem for mixed Gaussian variables: application to the auto-bispectral density. J. Statist. Phys. 136, 89102.CrossRefGoogle Scholar
Penrose, M. D. (2007). Gaussian limits for random geometric measures. Electron. J. Prob. 12, 9891035.Google Scholar
Rice, J. (1977). On generalized shot noise. Adv. Appl. Prob. 9, 553565.Google Scholar
Richard, F. and Biermé, H. (2010). Statistical tests of anisotropy for fractional Brownian textures. Application to full-field digital mammography. J. Math. Imaging Vision 36, 227240.Google Scholar
Schmitt, M. (1991). Estimation of the density in a stationary Boolean model. J. Appl. Prob. 28, 702708.Google Scholar
Schneider, R. and Weil, W. (2008). Stochastic and Integral Geometry. Springer, Berlin.Google Scholar
Stoyan, D., Kendall, W. S. and Mecke, J. (1995). Stochastic Geometry and Its Applications, 2nd edn. John Wiley, Chichester.Google Scholar