Syntactic Pattern Recognition

Syntactic Pattern Recognition, Applications

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 electro cardiograms and the recognition of speech pattems. The next five chapters deal with the syntactic recognition of two-dimensional pictorial pattems.

Syntactic Pattern Recognition

This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence.

Syntactic Pattern Recognition