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

  • Track 4: Algorithms
  • Conference paper
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Computing in the 90's (Great Lakes CS 1989)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 507))

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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.

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References

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Authors and Affiliations

  1. Dept. of Computer Science, Southern Illinois University at Carbondale, USA

    Michael S. Wainer

Authors
  1. Michael S. Wainer
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Editor information

Naveed A. Sherwani Elise de Doncker John A. Kapenga

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© 1991 Springer-Verlag Berlin Heidelberg

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Wainer, M.S. (1991). Massively parallel implementations of adaptively subdividing fractal generating algorithms with parameter extensions. In: Sherwani, N.A., de Doncker, E., Kapenga, J.A. (eds) Computing in the 90's. Great Lakes CS 1989. Lecture Notes in Computer Science, vol 507. Springer, New York, NY. https://doi.org/10.1007/BFb0038491

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  • DOI: https://doi.org/10.1007/BFb0038491

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97628-0

  • Online ISBN: 978-0-387-34815-5

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