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Scientific Applications

  • Kendall PrestonJr.
  • Michael J. B. Duff
Part of the Advanced Applications in Pattern Recognition book series (AAPR)

Abstract

This chapter describes applications of cellular automata to data which do not necessarily represent optical images, even though images can be used to represent the data, e.g., an electrostatic field. The array should therefore be regarded not as part of a vision system but rather as a computation and visualizing system. It will be shown that this approach to computation stimulates the conception of unusual computational algorithms which are not only capable of efficient implementation by the cellular automaton but also, in some cases, more efficient than when implemented on conventional serial machines.

Keywords

Cellular Automaton Scientific Application Minimum Path Electric Field Potential Minimum Fuel Consumption 
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 Science+Business Media New York 1984

Authors and Affiliations

  • Kendall PrestonJr.
    • 1
    • 2
    • 3
  • Michael J. B. Duff
    • 4
  1. 1.Carnegie-Mellon UniversityPittsburghUSA
  2. 2.University of PittsburghPittsburghUSA
  3. 3.Kensal ConsultingTusconUSA
  4. 4.University College LondonLondonEngland

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