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Approximation of Measures by Fractal Generation Techniques

  • Stephen Demko
Conference paper
  • 259 Downloads
Part of the Springer Series in Computational Mathematics book series (SSCM, volume 19)

Abstract

In this paper we discuss the constructive approximation aspects of a measure generating technique that was developed in the last decade by fractal geometers. Measures are generated as stationary distributions of Markov chains that are related to function iteration. The general framework provides a robust machine for constructing parametric families of measures and is, thus, very well suited for measure approximation problems. This formalism — called iterated function system theory — has found application in computer image synthesis and image compression. In fact some of the early work was motivated by a desire to create more realistic images for flight simulators. Other areas of potential application include dynamical systems, signal processing - especially chaotic signals, and quadrature theory.

Keywords

Invariant Measure Gaussian Quadrature Iterate Function System Bernoulli Polynomial Target Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1992

Authors and Affiliations

  • Stephen Demko
    • 1
  1. 1.School of MathematicsGeorgia Institute of TechnologyAtlantaUSA

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