Pattern Recognition Theory And Applications
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Syntactic and Structural Pattern Recognition
This book is currently the only one on this subject containing both introductory material and advanced recent research results. It presents, at one end, fundamental concepts and notations developed in syntactic and structural pattern recognition and at the other, reports on the current state of the art with respect to both methodology and applications. In particular, it includes artificial intelligence related techniques, which are likely to become very important in future pattern recognition.The book consists of individual chapters written by different authors. The chapters are grouped into broader subject areas like “Syntactic Representation and Parsing”, “Structural Representation and Matching”, “Learning”, etc. Each chapter is a self-contained presentation of one particular topic. In order to keep the original flavor of each contribution, no efforts were undertaken to unify the different chapters with respect to notation. Naturally, the self-containedness of the individual chapters results in some redundancy. However, we believe that this handicap is compensated by the fact that each contribution can be read individually without prior study of the preceding chapters. A unification of the spectrum of material covered by the individual chapters is provided by the subject and author index included at the end of the book.
Pattern Recognition Theory and Applications
Author: J. Kittler
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This book is the outcome of the successful NATO Advanced Study Institute on Pattern Recognition Theory and Applications, held at St. Anne's College, Oxford, in April 1981., The aim of the meeting was to review the recent advances in the theory of pattern recognition and to assess its current and future practical potential. The theme of the Institute - the decision making aspects of pattern recognition with the emphasis on the novel hybrid approaches - and its scope - a high level tutorial coverage of pattern recognition methodologies counterpointed with contrib uted papers on advanced theoretical topics and applications - are faithfully reflected by the volume. The material is divided into five sections: 1. Methodology 2. Image Understanding and Interpretation 3. Medical Applications 4. Speech Processing and Other Applications 5. Panel Discussions. The first section covers a broad spectrum of pattern recognition methodologies, including geometric, statistical, fuzzy set, syntactic, graph-theoretic and hybrid approaches. Its cove,r age of hybrid methods places the volume in a unique position among existing books on pattern recognition. The second section provides an extensive treatment of the topical problem of image understanding from both the artificial intelligence and pattern recognition points of view. The two application sections demonstrate the usefulness of the novel methodologies in traditional pattern 'recognition application areas. They address the problems of hardware/software implementation and of algorithm robustness, flexibility and general reliability. The final section reports on a panel discussion held during the Institute.
Markov Models for Pattern Recognition
Author: Gernot A. Fink
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-01-14
This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.