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A new class of markov processes for image encoding

Published online by Cambridge University Press:  01 July 2016

Michael F. Barnsley*
Affiliation:
Georgia Institute of Technology
John H. Elton*
Affiliation:
Georgia Institute of Technology
*
Postal address for both authors: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Postal address for both authors: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Abstract

A new class of iterated function systems is introduced, which allows for the computation of non-compactly supported invariant measures, which may represent, for example, greytone images of infinite extent. Conditions for the existence and attractiveness of invariant measures for this new class of randomly iterated maps, which are not necessarily contractions, in metric spaces such as , are established. Estimates for moments of these measures are obtained.

Special conditions are given for existence of the invariant measure in the interesting case of affine maps on . For non-singular affine maps on , the support of the measure is shown to be an infinite interval, but Fourier transform analysis shows that the measure can be purely singular even though its distribution function is strictly increasing.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 1988 

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References

[1] Barnsley, M. (1986) Fractal functions and interpolation. Constructive Approximation 2, 303329.CrossRefGoogle Scholar
[2] Barnsley, M. and Demko, S. (1985) Iterated function systems and the global construction of fractals, Proc. R. Soc. London A 399, 243275.Google Scholar
[3] Barnsley, M., Demko, S., Elton, J. and Geronimo, J. Attractors for iterated function systems with place dependent probabilities. In preparation.Google Scholar
[4] Barnsley, M., Ervin, V., Hardin, D. and Lancaster, J. (1986) Solution of an inverse problem for fractals and other sets. Proc. Nat. Acad. Sci. U.S.A. 83, 19751977.CrossRefGoogle ScholarPubMed
[5] Breiman, L. (1968) Probability. Addison-Wesley, Reading, Mass.Google Scholar
[6] Demko, S., Hodges, L. and Naylor, B. (1985) Construction of fractal objects with iterated function systems. Computer Graphics 19, 271278.CrossRefGoogle Scholar
[7] Diaconis, P. and Shashahani, M. (1986) Products of random matrices and computer image generation. Contemporary Math. 50, 173182.CrossRefGoogle Scholar
[8] Dubins, L. E. and Freedman, D. A. (1966) Invariant probabilities for certain Markov processes. Ann. Math. Statist. 32, 837848.CrossRefGoogle Scholar
[9] Erdös, P. (1939) On a family of symmetric Bernoulli convolutions. Amer. J. Math. 61, 974976.CrossRefGoogle Scholar
[10] Garsia, A. (1962) Arithmetic properties of Bernoulli convolutions. Trans. Amer. Math. Soc. 102, 409432.CrossRefGoogle Scholar
[11] Hewitt, E. and Stromberg, K. (1965) Real and Abstract Analysis. Springer-Verlag, New York.Google Scholar
[12] Hutchinson, J. (1981) Fractals and self-similarity. Indiana U. J. Math. 30, 713747.CrossRefGoogle Scholar
[13] Karlin, S. (1953) Some random walks arising in learning models. Pacific J. Math. 3, 725756.CrossRefGoogle Scholar
[14] Mandelbrot, B. (1982) The Fractal Geometry of Nature. W. H. Freeman, San Francisco.Google Scholar
[15] Riesz, F. and Sz.-Nagy, B. (1955) Functional Analysis. Frederick Ungar, New York.Google Scholar