Digital Pattern Recognition
1st Edition
Published on 8. March 2013
X, 206 pages
978-3-642-96303-2 (ISBN)
Description
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) approach. Since the area of pattern recognition is still far from its maturity, many new research results, both in theory and in applications, are continuously produced. The purpose of this monograph is to provide a concise summary of the major recent developments in pattern recognition. The five main chapters (Chapter 2-6) in this book can be divided into two parts. The first three chapters concern primarily with basic techniques in pattern recognition. They include statistical techniques, clustering analysis and syntactic techniques. The last two chapters deal with applications; namely, picture recognition, and speech recognition and understanding. Each chapter is written by one or two distinguished experts on that subject. The editor has not attempted to impose upon the contributors to this volume a uniform notation and terminol ogy, since such notation and terminology does not as yet exist in pattern recognition.
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K. S. Fu
Digital Pattern Recognition
Book
04/1976
Springer
€34.91
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Content
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.
