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Massively parallel implementations of adaptively subdividing fractal generating algorithms with parameter extensions

  • Michael S. Wainer
Track 4: Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 507)

Abstract

Fractal approximations are used to generate data to mimic natural objects for realistic image synthesis. While many algorithms for generating fractal data for this purpose exist, the adaptive subdivision methods are in widespread use because of their computational efficiency. This paper discusses the adaptation of the triangular and rectangular subdivision algorithms originally described in [1] to massively parallel SIMD architectures. In addition, the algorithms are enhanced to permit more control over the characteristics of the surfaces.

Keywords

Stochastic Component Child Type Triangular Patch Adjacent Patch Subdivision Algorithm 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Michael S. Wainer
    • 1
  1. 1.Dept. of Computer ScienceSouthern Illinois University at CarbondaleUSA

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