Elsevier

Pattern Recognition

Volume 7, Issue 3, September 1975, Pages 109-116
Pattern Recognition

Some general remarks about pattern recognition; its definition; its relation with other disciplines; a literature survey

https://doi.org/10.1016/0031-3203(75)90021-7Get rights and content

Abstract

A survey is given of definitions and descriptions, taken from literature, concerning the terms: pattern; recognition; pattern recognition; and of related terms like classification, cognition, etc. Both very general definitions and more specific ones are quoted. The terms mentioned, appear not to have an identical meaning with different authors.

Relations between pattern recognition and other disciplines are discussed and graphically indicated in a global way. Some tentative conclusions are given about possible future activities, concerning the definition of general terms in the field of pattern recognition.

References (42)

  • L. Uhr

    Pattern Recognition, Learning and Thought

  • V.E. Giuliano

    How we find patterns

    International Science and Technology

    (1967)
  • W.S. Meisel

    Computer-Oriented Approaches to Pattern Recognition

  • K.M. Sayre

    Recognition, A Study in the Philosophy of Artificial Intelligence

    (1965)
  • B. Blesser et al.

    A theoretical approach for character recognition based on phenomenological attributes

  • R.O. Duda et al.

    Pattern Classification and Scene Analysis

  • A.S. Sebestyen

    Decision-Making Processes in Pattern Recognition

  • S. Kaneff

    Pattern cognition and the organization of information

  • Yu-Chi Ho et al.

    On pattern classification algorithms, introduction and survey

  • J.M. Blin et al.

    Discriminant functions and majority voting

  • A.S. Sebestyen

    Decision-Making Processes in Pattern Recognition

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