Digital Pattern Recognition

by K. S. Fu (Editor), T. M. Cover (Contribution by), Edwin Diday (Contribution by), K. S. Fu (Contribution by), A. Rosenfeld (Contribution by)

Overview

Since its publication in 1976, the original volume has been warmly received. We have decided to put out this updated paperback edition so that the book can be more accessible to students. This paperback edition is essentially the same as the original hardcover volume except for the addition of a new chapter (Chapter 7) which reviews the recent advances in pattern recognition and image processing. Because of the limitations of length, we can only report the highlights and point the readers to the literature. A few typographical errors in the original edition were corrected. We are grateful to the National Science Foundation and the Office of Naval Research for supporting the editing of this book as well as the work described in Chapter 4 and a part of Chapter 7. West Lafayette, Indiana March 1980 K. S. Fu Preface to the First Edition During the past fifteen years there has been a considerable growth of interest in problems of pattern recognition. Contributions to the blossom of this area have come from many disciplines, including statistics, psychology, linguistics, computer science, biology, taxonomy, switching theory, communication theory, control theory, and operations research. Many different approaches have been proposed and a number of books have been published. Most books published so far deal with the decision-theoretic (or statistical) approach or the syntactic (or linguistic) is still far from its maturity, many approach.

Product Details

ISBN-13:	9783540102076
Publisher:	Springer Berlin Heidelberg
Publication date:	10/24/1980
Series:	Communication and Cybernetics , #10
Edition description:	Softcover reprint of the original 2nd ed. 1980
Pages:	234
Product dimensions:	6.69(w) x 9.61(h) x 0.02(d)

Table of Contents

1. Introduction.- 1.1 What is Pattern Recognition?.- 1.2 Approaches to Pattern Recognition.- 1.3 Basic Non-Parametric Decision — Theoretic Classification Methods.- 1.4 Training in Linear Classifiers.- 1.5 Bayes (Parametric) Classification.- 1.6 Sequential Decision Model for Pattern Classification.- 1.7 Bibliographical Remarks.- References.- 2. Topics in Statistical Pattern Recognition.- 2.1 Nonparametric Discrimination.- 2.2 Learning with Finite Memory.- 2.3 Two-Dimensional Patterns and Their Complexity.- References.- 3. Clustering Analysis.- 3.1 Introduction.- 3.2 The Initial Description.- 3.3 Properties of a Cluster, a Clustering Operator and a Clustering Process.- 3.4 The Main Clustering Algorithms.- 3.5 The Dynamic Clusters Method.- 3.6 Adaptive Distances in Clustering.- 3.7 Conclusion and Future Prospects.- References.- 4. Syntactic (Linguistic) Pattern Recognition..- 4.1 Syntactic (Structural) Approach to Pattern Recognition.- 4.2 Linguistic Pattern Recognition System.- 4.3 Selection of Pattern Primitives.- 4.4 Pattern Grammar.- 4.5 High-Dimensional Pattern Grammars.- 4.6 Syntax Analysis as Recognition Procedure.- 4.7 Concluding Remarks.- References.- 5. Picture Recognition.- 5.1 Introduction.- 5.2 Properties of Regions.- 5.3 Detection of Objects.- 5.4 Properties of Detected Objects.- 5.5 Object Extraction.- 5.6 Properties of Extracted Objects.- 5.7 Representation of Objects and Pictures.- References.- 6. Speech Recognition and Understanding..- 6.1 Principles of Speech, Recognition, and Understanding.- 6.2 Recent Developments in Automatic Speech Recognition.- 6.3 Speech Understanding.- 6.4 Assessment of the Future.- References.- 7. Recent Developments in Digital Pattern Recognition..- 7.1 A General Viewpoint of Pattern Recognition.- 7.2 Tree Grammars forSyntactic Pattern Recognition.- 7.3 Syntactic Pattern Recognition Using Shastic Languages.- 7.4 Error-Correcting Parsing.- 7.5 Clustering Analysis for Syntactic Patterns.- 7.6 Picture Recognition.- 7.7 Speech Recognition and Understanding.- References.