Hostname: page-component-77c89778f8-7drxs Total loading time: 0 Render date: 2024-07-21T10:04:21.594Z Has data issue: false hasContentIssue false

Estimation of the mean normal measure from flat sections

Published online by Cambridge University Press:  01 July 2016

Markus Kiderlen*
Affiliation:
University of Aarhus
*
Postal address: Department of Mathematical Sciences, University of Aarhus, Ny Munkegade Build. 1530, DK-8000 Aarhus C, Denmark. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We discuss the determination of the mean normal measure of a stationary random set Z ⊂ ℝd by taking measurements at the intersections of Z with k-dimensional planes. We show that mean normal measures of sections with vertical planes determine the mean normal measure of Z if k ≥ 3 or if k = 2 and an additional mild assumption holds. The mean normal measures of finitely many flat sections are not sufficient for this purpose. On the other hand, a discrete mean normal measure can be verified (i.e. an a priori guess can be confirmed or discarded) using mean normal measures of intersections with m suitably chosen planes when m ≥ ⌊d / k⌋ + 1. This even holds for almost all m-tuples of k-dimensional planes are viable for verification. A consistent estimator for the mean normal measure of Z, based on stereological measurements in vertical sections, is also presented.

Type
Stochastic Geometry and Statistical Applications
Copyright
Copyright © Applied Probability Trust 2008 

References

Beneš, V. and Rataj, J. (2004). Stochastic Geometry: Selected Topics. Kluwer, Boston, MA.Google Scholar
Beneš, V. and Saxl, I. (2005). Stereological estimation of the rose of directions from the rose of intersections. In Recent Advances in Applied Probability, eds Baeza-Yates, R. et al., Springer, New York, pp. 6596.Google Scholar
Fallert, H., Goodey, P. and Weil, W. (1997). Spherical projections and centrally symmetric sets. Adv. Math. 129, 301322.Google Scholar
Gardner, R. (2006). Geometric Tomography, 2nd edn. Cambridge University Press.Google Scholar
Gardner, R. and Gritzmann, P. (1997). Discrete tomography: determination of finite sets by X-rays. Trans. Amer. Math. Soc. 349, 22712295.Google Scholar
Gardner, R. J., Kiderlen, M. and Milanfar, P. (2006). Convergence of algorithms for reconstructing convex bodies and directional measures. Ann. Statist. 34, 13311374.Google Scholar
Goodey, P., Kiderlen, M. and Weil, W. (1998). Section and projection means of convex bodies. Monatsh. Math. 126, 3754.Google Scholar
Goodey, P., Kiderlen, M. and Weil, W. (2008). Spherical projections and liftings in geometric tomography. Submitted.Google Scholar
Gutkowski, P., Jensen, E. B. V. and Kiderlen, M. (2004). Directional analysis of digitized three-dimensional images by configuration counts. J. Microscopy 216, 175185.Google Scholar
Hug, D., Last, G. and Weil, W. (2004). A local Steiner-type formula for general closed sets and applications. Math. Z. 246, 237272.Google Scholar
Kiderlen, M. (1999). Schnittmittelungen und äquivariante Endomorphismen konvexer Körper. , University of Karlsruhe.Google Scholar
Kiderlen, M. (2001). Non-parametric estimation of the directional distribution of stationary line and fibre processes. Adv. Appl. Prob. 33, 624.Google Scholar
Kiderlen, M. (2002). Determination of the mean normal measure from isotropic means of flat sections. Adv. Appl. Prob. 34, 505519.Google Scholar
Kiderlen, M. and Jensen, E. B. V. (2003). Estimation of the directional measure of planar random sets by digitization. Adv. Appl. Prob. 35, 583602.CrossRefGoogle Scholar
Kiderlen, M. and Weil, W. (1999). Measure-valued valuations and mixed curvature measures of convex bodies. Geom. Dedicata 76, 291329.Google Scholar
Männle, S. (2002). Ein Kleinste-Quadrat-Schätzer des Richtungsmasses für stationäre Geradenprozesse. . University of Karlsruhe.Google Scholar
Molchanov, I. (1995). Statistics of the Boolean model: from the estimation of means to the estimation of distributions. Adv. Appl. Prob. 27, 6386.Google Scholar
Molchanov, I. and Stoyan, D. (1994). Directional analysis of fibre processes related to Boolean models. Metrika 41, 183199.Google Scholar
Pohlmann, S., Mecke, J. and Stoyan, D. (1981). Stereological formulas for stationary surface processes. Math. Operat. Statist. Ser. Statist. 12, 429440.Google Scholar
Rataj, J. (1996). Estimation of oriented direction distribution of a planar body. Adv. Appl. Prob. 28, 394404.Google Scholar
Rataj, J. (1999). Translative and kinematic formulae for curvature measures of flat sections. Math. Nachr. 197, 89101.Google Scholar
Rataj, J. and Zähle, M. (2001). Curvatures and currents for unions of sets with positive reach. II. Ann. Global Anal. Geom. 20, 121.Google Scholar
Schneider, R. (1993). Convex Bodies: The Brunn–Minkowski Theory. Cambridge University Press.Google Scholar
Schneider, R. (2001). On the mean normal measures of a particle process. Adv. Appl. Prob. 33, 2538.Google Scholar
Schneider, R. and Weil, W. (1992). Integralgeometrie. Teubner, Stuttgart.Google Scholar
Schneider, R. and Weil, W. (2000). Stochastische Geometrie. Teubner, Stuttgart.Google Scholar
Stoyan, D. and Beneš, V. (1991). Anisotropy analysis for particle systems. J. Microsc. 164, 159168.Google Scholar
Stoyan, D., Kendall, W. and Mecke, J. (1995). Stochastic Geometry and its Applications, 2nd edn, John Wiley, New York.Google Scholar
Weil, W. (1982). Zonoide und verwandte Klassen konvexer Körper. Monatsh. Math. 94, 7384.Google Scholar
Weil, W. (1995). The estimation of mean shape and mean particle number in overlapping particle systems in the plane. Adv. Appl. Prob. 27, 102119.Google Scholar
Weil, W. (1997). Mean bodies associated with random closed sets. Suppl. Rend. Circ. Mat. Palermo Ser. II 50, 387412.Google Scholar
Weil, W. (1997). The mean normal distribution of stationary random sets and particle processes. In Proc. Internat. Symp. Adv. Theory Appl. Random Sets (Fontainebleau, ed. Jeulin, D., 1986). World Scientific, Singapore, pp. 2133.Google Scholar
Weil, W. (1999). Intensity analysis of Boolean models. Pattern Recognition 32, 16751684.CrossRefGoogle Scholar
Ziegel, J. and Kiderlen, M. (2008). Estimation of surface area and surface area measure of a three-dimensional set from digitizations. SubmittedGoogle Scholar