Syntactic Pattern Recognition, Applications
β Scribed by K. S. Fu (auth.), Professor King Sun Fu PhD (eds.)
- Publisher
- Springer-Verlag Berlin Heidelberg
- Year
- 1977
- Tongue
- English
- Leaves
- 278
- Series
- Communication and Cybernetics 14
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
The many different mathematical techniques used to solve pattem recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach. In the decision-theoretic approach, aset of characteristic measurements, called features, are extracted from the pattems. Each pattem is represented by a feature vector, and the recognition of each pattem is usually made by partitioning the feature space. Applications of decision-theoretic approach indude character recognition, medical diagnosis, remote sensing, reliability and socio-economics. A relatively new approach is the syntactic approach. In the syntactic approach, ea ch pattem is expressed in terms of a composition of its components. The recognition of a pattem is usually made by analyzing the pattem structure according to a given set of rules. Earlier applications of the syntactic approach indude chromosome dassification, English character recognition and identification of bubble and spark chamber events. The purpose of this monograph is to provide a summary of the major reeent applications of syntactic pattem recognition. After a brief introduction of syntactic pattem recognition in Chapter 1, the nin e mai n chapters (Chapters 2-10) can be divided into three parts. The first three chapters concem with the analysis of waveforms using syntactic methods. Specific application examples indude peak detection and interpretation of electroΒ cardiograms and the recognition of speech pattems. The next five chapters deal with the syntactic recognition of two-dimensional pictorial pattems.
β¦ Table of Contents
Front Matter....Pages I-XI
Introduction to Syntactic Pattern Recognition....Pages 1-30
Peak Recognition in Waveforms....Pages 31-49
Electrocardiogram Interpretation Using a Stochastic Finite State Model....Pages 51-64
Syntactic Recognition of Speech Patterns....Pages 65-94
Chinese Character Recognition....Pages 95-123
Shape Discrimination....Pages 125-145
Two-Dimensional Mathematical Notation....Pages 147-177
Fingerprint Classification....Pages 179-214
Modeling of Earth Resources Satellite Data....Pages 215-242
Industrial Objects and Machine Parts Recognition....Pages 243-267
Back Matter....Pages 268-272
β¦ Subjects
Science, general
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<p>Thirty years ago pattern recognition was dominated by the learning machine concept: that one could automate the process of going from the raw data to a classifier. The derivation of numerical features from the input image was not considered an important step. One could present all possible featur