Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T06:43:35.611Z Has data issue: false hasContentIssue false

Mixing Markov Chains and Their Images

Published online by Cambridge University Press:  27 July 2009

Michael F. Barnsley
Affiliation:
School of Mathematics Georgia Institute of Technology, Atlanta, Georgia 30332
Marc A. Berger
Affiliation:
Department of Theoretical MathematicsWeizmann Institute of Science, Rehovot 76100, Israel and Department of MathematicsCarnegie-Mellon University, Pittsburgh, Pennsylvania 15213
H. Meté Soner
Affiliation:
Department of MathematicsCarnegie-Mellon University, Pittsburgh, Pennsylvania 15213

Abstract

Recently, orbits of two-dimensional Markov chains have been used to generate computer images. These chains evolve according to products of i.i.d. affine maps. We deal with mixing models, whereby one mixes together several of these Markov chains, so as to create a mixed image. These mixtures involve starting one Markov chain off at the stationary distribution of another, and then running it for a geometrically distributed number of steps. We use this to analyze various mixing scenarios.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1988

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Barnsley, M.F. & Demko, S.G. (1985). Iterated function systems and the global construction of fractals, Proceedings of the Royal Society London A399: 243275.Google Scholar
2.Barnsley, M.F., Elton, J.H., & Hardin, D.P. (1986). Recurrent iterated function systems. Georgia Institute of Technology. Preprint.Google Scholar
3.Barnsley, M.F. & Sloan, A.D. (1987). Image compression. Computer Graphics World 10: 107108.Google Scholar
4.Berger, M.A. Images generated by orbits of 2-D Markov chains. CHANCE, to appear.CrossRefGoogle Scholar
5.Berger, M.A.Encoding images through transition probabilities. Proceedings of the Sixth International Conference on Mathematical Modeling, Mathl. Comput. Modelling 11 (1988), 575577.Google Scholar
6.Berger, M.A. & Amit, Y. (1986). Products of random affine maps. Weizmann Institute of Science. Preprint.Google Scholar
7.Berger, M.A. & Soner, H.M.Random walks generated by affine mappings. J. Theor. Prob. 1 (1988), 239254.CrossRefGoogle Scholar
8.Demko, S.G., Hodges, L. & Naylor, B. (1985). Construction of fractal objects with iterated function systems. SIGGRAPH 19: 271278.CrossRefGoogle Scholar
9.Diaconis, P. & Shahshahani, M. (1986). Products of random matrices and computer image generation. Contemporary Mathematics 50:173182.CrossRefGoogle Scholar
10.Hutchinson, J. (1981). Fractals and self-similarity. Indiana University Journal of Mathematics 30: 713747.CrossRefGoogle Scholar
11.Peterson, I. (1987). Packing it in. Science News 131: 283285.CrossRefGoogle Scholar
12.Barnsley, M.F. & Sloan, A.D. (1988). A better way to compress images. Byte 13: 215223.Google Scholar