Parallel Guessing: A Strategy for High-Speed Computation

Abstract

Attempts have been made to speed up image-understanding computation involving conventional serial algorithms by decomposing these algorithms into portions that can be computed in parallel. Because many classes of algorithms do not readily decompose, one seeks some other basis for parallelism (i.e., for using additional hardware to obtain higher processing speed). In this paper we argue that "parallel guessing" for image analysis is a useful approach, and that several recent IU algorithms are based on this concept. Problems suitable for this approach have the characteristic that either "distance" from a true solution, or the correctness of a guess, can be readily checked. We review image-analysis algorithms having a parallel guessing or randomness flavor. We envision a parallel set of computers, each of which carries out a computation on a data set using some random or guessing process, and communicates the "goodness" of its result to its co-workers through a "blackboard" mechanism.

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Document Details

Document Type
Technical Report
Publication Date
Sep 19, 1984
Accession Number
ADA461630

Entities

People

  • Martin A. Fischler
  • Oscar Firschein

Organizations

  • SRI International

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Computations
  • Data Sets
  • Information Operations
  • Mathematical Analysis
  • Mathematics
  • Recognition
  • Target Recognition

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Theoretical Analysis.