Overview
- Covers speech, handwriting recognition, and biological sequence analysis
- Both theoretical foundations as well as methods required to build successful applications in practice are presented
- Suitable for experts as well as for practitioners
- Includes supplementary material: sn.pub/extras
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Table of contents (15 chapters)
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Front Matter
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Introduction
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Application Areas
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Theory
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Front Matter
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Systems
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Front Matter
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Back Matter
Keywords
About this book
Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications.
Additionally, the actual use of the technology in the three main application areas of pattern recognition methods based on Markov- Models - namely speech recognition, handwriting recognition, and biological sequence analysis - are demonstrated.
Reviews
"The practice part makes the book unique among many other pattern recognition textbooks. It discusses implementation details that are often ignored in the literature, but are important in constructing a working system. … Overall, the book is well written and clear ... It is suited not to those who want to learn the basics of pattern recognition, but to those who want to learn the state of the art of speech, character, and DNA sequence recognition problems from the perspective of the practitioner and designer. … The depth and breadth of the treatment is right for the intent of the book."
(T. Kubota, Lewisburg, PA, in: Computing Reviews, May 2009)
Authors and Affiliations
About the author
University of Erlangen-Nuremberg, Erlangen, Germany, in 1991.
He recieved a Ph.D. degree in computer science in 1995 and
the venia legendi in applied computer science in 2002 both
from Bielefeld University, Germany.
Currently, he is professor for Pattern Recognition in Embedded Systems
at the University of Dortmund, Germany, where he also heads the
Intelligent Systems Group at the Robotics Research Institute.
His reserach interests lie in the development and application of
pattern recognition methods in the fields of man machine interaction,
multimodal machine perception including speech and image processing,
statistical pattern recognition, handwriting recognition, and the
analysis of genomic data.
Bibliographic Information
Book Title: Markov Models for Pattern Recognition
Book Subtitle: From Theory to Applications
Authors: Gernot A. Fink
DOI: https://doi.org/10.1007/978-3-540-71770-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2008
Edition Number: 1
Number of Pages: XII, 248
Number of Illustrations: 51 b/w illustrations
Additional Information: Original German edition published by Teubner, 2003
Topics: Pattern Recognition, Image Processing and Computer Vision, Natural Language Processing (NLP), Artificial Intelligence