Pattern Recognition: Concepts, Methods and Applications

Assembly Automation

ISSN: 0144-5154

Article publication date: 1 December 2002




Rigelsford, J. (2002), "Pattern Recognition: Concepts, Methods and Applications", Assembly Automation, Vol. 22 No. 4.



Emerald Group Publishing Limited

Copyright © 2002, MCB UP Limited

Pattern Recognition: Concepts, Methods and Applications

Pattern Recognition: Concepts, Methods and Applications

J.P. Marques de SáSpringer2001318 pp.ISBN 3-540-42297-8£37.00 (hardback with CD-ROM)

Keywords: Pattern recognition

'Pattern Recognition' presents methods and techniques that are suitable for practical application in areas including robot assisted manufacture, medical diagnostic systems, forecast of economic variables, exploration of Earth’s resources, and satellite data analysis.

The book contains six chapters and is accompanied by a CD-ROM. Chapter 1 introduces the Basic Notions of pattern recognition, and discusses topics including: object recognition; pattern similarity and pattern recognition tasks; classes, patterns and features; and pattern recognition projects.

Decision regions and functions; feature space metrics; the covariance matrix; and the dimensionality problem, are amongst the topics presented in chapter 2, Pattern Discrimination. Chapters 3 and 4 discuss Data Clustering, and Statistical Classification, respectively. Subjects covered in these sections include: unsupervised classification; the standardisation issue; tree and k-means clustering; linear discriminants; Bayesian classification; feature selection; and statistical classifiers in data mining.

Chapter 5 applies Neural Networks to pattern recognition and explains the different types of neural networks and their performance. It also addresses LMS adjusted discriminants; multi-layer perceptrons; approximation functions in neural network training; and neural networks in data mining. The final chapter of the book, Structural Pattern Recognition, discusses pattern primitives, structural representations, syntactic analysis, and structural matching.

Each chapter contains a bibliography, while chapters 2-6 also include exercises. Appendix A and B describe the datasets and analysis tools found on the CD. Appendix C presents the Orthonormal Transformation.

This book provides comprehensive, non- specialist coverage of pattern recognition. Although primarily aimed at undergraduate and graduate engineering and computer science students, its clear and practical coverage also makes it suitable for physicians, biologists, geologists and economists.

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