Languages, Automata and Classes of Chain-Encoded Patterns

Abstract

By treating patterns as statements in a two-dimensional language, it is possible to apply linguistic theory to pattern analysis and recognition. This report presents an approach to a classification scheme for pattern languages that could provide information about types of programs and computation facilities capable of meeting particular pattern analysis and recognition requirements. Consideration is restricted to line patterns encoded in the chain code developed by Freeman. This encoding method represents a line pattern by a sequence of octal digits called a chain. Results can be extended to other forms of encoding when translators between codes can be built. The report compares languages formed by Boolean functions of languages and by the concatenation of strings of a number of languages with pattern languages. Pattern languages based on families of equations in two variables and formed from chains of straight lines, circles, and circular arcs are related to string language classes. Pattern properties, including closure, self-intersection, convexity, and periodicity, are examined. Pattern languages are also considered that are similar in various ways to an arbitrary given chain. Although the patterns considered in this report are relatively simple ones, it appears possible by means of language rules of a table-driven pattern analyzer to extend the approach to pattern classes containing more complex patterns.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1967
Accession Number
AD0668081

Entities

People

  • Jerome Feder

Organizations

  • New York University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Automata
  • Coding
  • Computations
  • Context Free Grammars
  • Detection
  • Electrical Engineering
  • Engineering
  • Grammars
  • Information Science
  • Language
  • Literature Surveys
  • New York
  • Phrase Structure Grammars
  • Shape
  • Specifications
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.