Stochastic syntactic analysis for programmed grammars and syntactic pattern recognition

Thomas S Huang, K. S. Fu

Research output: Contribution to journalArticle

Abstract

In this paper, some properties of stochastic context-free programmed grammars arepresented. By illustrative examples, the class of stochastic context-free programmed grammars is shown to be quite effective in characterizing the structures of some pictorial patterns. Two stochastic syntactic-analysis algorithms for stochastic context-free programmed grammars are introduced. It is shown that the stochastic analyzer with a fixed strategy would require the least average number of steps to recognize the language generated by a stochastic context-free programmed grammar, while the stochastic syntactic analyzer with a random strategy would be useful in the case where the probability information associated with the stochastic context-free programmed grammar is incomplete or inaccurately known. The performance of the stochastic syntactic analyzer with a random strategy would be better than that of the nondeterministic syntactic analyzer proposed earlier. The use of the proposed stochastic syntactic-analysis algorithm with a fixed strategy for syntactic pattern recognition is illustrated by a chromosome patternclassification experiment.

Original languageEnglish (US)
Pages (from-to)257-283
Number of pages27
JournalComputer Graphics and Image Processing
Volume1
Issue number3
DOIs
StatePublished - Nov 1972
Externally publishedYes

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ASJC Scopus subject areas

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

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