Book ChapterDOI
Syntactic (Linguistic) Pattern Recognition
K. S. Fu
- pp 95-134
TLDR
This chapter provides an overview, illustrated by a great number of examples, of the syntactic (linguistic) pattern recognition of one-dimensional and high-dimensional grammars.Abstract:
This chapter provides an overview, illustrated by a great number of examples, of the syntactic (linguistic) pattern recognition Languages are used to describe patterns, and syntax analysis procedures are employed as recognition procedures Methods for the selection of pattern primitives are presented Both one-dimensional (string) and high-dimensional grammars are discussed and their applications to one-dimensional and high-dimensional patterns demonstrated Problems for further research are suggestedread more
Citations
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Proceedings Article
Graphical applications of L-systems
TL;DR: The idea is to generate a string of symbols using an L−system, and to interpret this string as a sequence of commands which control a "turtle", which can be used to create a variety of fractal curves.
Journal ArticleDOI
Machine Intelligence 6. Edited by B. Meltzer and D. Michie. Pp. 525. £10. 1971. (Edinburgh University Press.)
Journal ArticleDOI
Contextual classification of multispectral pixel data
Josef Kittler,J. Föglein +1 more
TL;DR: Contextual statistical decision rules for classification of lattice-structured data such as pixels in multispectral imagery are developed and their recursive implementation is shown to have a strong resemblance to relaxation algorithms.
Journal ArticleDOI
Contextual Pattern Recognition Applied to Cloud Detection and Identification
Josef Kittler,D. Pairman +1 more
TL;DR: A general contextual classification rule is developed which is then simplified under some realistic assumptions made and confirms its superiority over the conventional Bayes rule and provides justification for the assumption made.
Journal ArticleDOI
On compatibility and support functions in probabilistic relaxation
Josef Kittler,J Föglein +1 more
TL;DR: It is discussed how the choice of heuristic compatibility and support functions should depend on the type of contextual information to be exploited (spatial relationship between objects, correlation of observations).
References
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Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
On the Encoding of Arbitrary Geometric Configurations
TL;DR: It is shown that one can determine through the use of relatively simple numerical techniques whether a given arbitrary plane curve is open or closed, whether it is singly or multiply connected, and what area it encloses.