By: Michail I. Schlesinger, Vaclav Hlavac

Paperback

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This monograph explores the close relationship of various well-known pattern recognition problems that have so far been considered independent. These relationships became apparent with the discovery of formal procedures for addressing known problems and their generalisations. The generalised problem formulations were analysed mathematically and unified algorithms were found. The main scientific contribution of this book is the unification of two main streams in pattern recognition - the statistical one and the structural one. The material is presented in the form of ten lectures, each of which concludes with a discussion with a student.
It provides new views and numerous original results in their field. Written in an easily accessible style, it introduces the basic building blocks of pattern recognition, demonstrates the beauty and the pitfalls of scientific research, and encourages good habits in reading mathematical text.

Preface. Lecture 1. Bayesian statistical decision making. Lecture 2. Non-Bayesian statistical decision making. Lecture 3. Two statistical models of the recognised object. Lecture 4. Learning in pattern recognition. Lecture 5. Linear discriminant function. Lecture 6. Unsupervised Learning. Lecture 7. Mutual relationship of statistical and structural recognition. Lecture 8. Recognition of Markovian sequences. Lecture 9. Regular languages and corresponding pattern recognition tasks. Lecture 10. Context-free languages, their 2-D generalisation, related tasks. Bibliography. Index.

ISBN: 9789048160273
ISBN-10: 9048160278
Series: Computational Imaging and Vision
Audience: Professional
Format: Paperback
Language: English
Number Of Pages: 541
Available: 31st July 2013
Publisher: SPRINGER VERLAG GMBH
Dimensions (cm): 24.0 x 16.0