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Pattern Classification and Scene Analysis [Englisch] [Gebundene Ausgabe]

Richard O. Duda , Peter E. Hart
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Kurzbeschreibung

Juni 1973
Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

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Produktinformation

  • Gebundene Ausgabe: 482 Seiten
  • Verlag: John Wiley & Sons Inc (Juni 1973)
  • Sprache: Englisch
  • ISBN-10: 0471223611
  • ISBN-13: 978-0471223610
  • Größe und/oder Gewicht: 23,1 x 15,5 x 3 cm
  • Durchschnittliche Kundenbewertung: 4.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 378.612 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

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Produktbeschreibungen

Klappentext

Pattern Classification and Scene Analysis By Richard O. Duda and Peter E. Hart Here is a unified, Comprehensive, and up-to-date treatment of the theoretical principles of pattern recognition. These principles are applicable to a great variety of problems of current interest, such as character recognition, speech recognition, speaker identification, fingerprint recognition, the analysis of biomedical photographs, aerial photoreconnaissance, automatic inspection for industrial quality control, and visual systems for robots. Throughout Pattern Classification and Scene Analysis, the authors have balanced their presentation to reflect the relative importance of the many theoretical topics in the field. Pattern Classification and Scene Analysis is the first book to provide comprehensive coverage of both statistical classification theory and computer analysis of pictures. Part I covers Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, and clustering. Part II describes many techniques of current interest in automatic scene analysis, including preprocessing of pictorial data, spatial filtering, shape-description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis. Although the theories and techniques of pattern recognition are largely mathematical, the authors have been more concerned with providing insight and understanding than with establishing rigorous mathematical foundations. The many illustrative examples, plausibility arguments, and discussions of the behavior of solutions reflect this concern. Extensive bibliographical and historical remarks at the end of each chapter further enhance the presentation. Standard notation is used wherever possible, and a comprehensive index is included. Typical first-year graduate students will find most of the mathematical arguments well within their grasp. Because the exposition is clear and balanced, Pattern Classification and Scene Analysis is suitable for both college and professional use. In particular, it will appeal to graduate students and professionals in the fields of computer science, electrical engineering, and statistics. Students and professionals in psychology, biomedical science, meteorology, and biology will also find it of value for the light it sheds on such areas as visual perception, image processing, and numerical taxonomy.

Buchrückseite

Introduction to Mathematical Techniques in Pattern Recognition by Harry C. Andrews This volume is one of the first cohesive treatments of the use of mathematics for studying interactions between various recognition environments. It brings together techniques previously scattered throughout the literature and provides a concise common notation that will facilitate the understanding and comparison of the many aspects of mathematical pattern recognition. The contents of this volume are divided into five interrelated subject areas: Feature Selection, Distribution Free Classification, Statistical Classification, Nonsupervised Learning, and Sequential Learning. Appendices describing specific aspects of feature selection and extensive reference and bibliographies are included. 1972 253 pp. Threshold Logic and its Applications by Saburo Muroga This is the first in–depth exposition of threshold logic and its applications using linear programming and integer programming as optimization tools. It presents threshold logic as a unified theory of conventional simple gates, threshold gates and their networks. This unified viewpoint explicitly reveals many important properties that were formerly concealed in the framework of conventional switching theory (based essentially on and, or and not gates). 1971 478 pp. Knowing and Guessing A Quantitative Study of Inference and Information By Satosi Watanabe This volume presents a coherent theoretical view of a field now split into different disciplines: philosophy, information science, cybernetics, psychology, electrical engineering, and physics. The target of investigation is the cognitive process of knowing and guessing. In contrast to traditional philosophy, the approach is quantitative rather than qualitative. The study is formal in the sense that the author is not interested in the contents of knowledge or the physiological mechanism of the process of knowing. "The author’s style is lucid, his comments are illuminating. The result is a fascinating book, which will be of interest to scientists in many different fields." — Nature 1969 592 pp.

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4.0 von 5 Sternen Gotta have it 10. Februar 1997
Von Ein Kunde
Format:Gebundene Ausgabe
While it's becoming a little dated (no problem, v.2 is in the works), this is the "must have" book for anyone concerned with pattern classification and scene analysis - not just computer vision, but anything related to pattern classification. It introduces all of the basic techniques and approaches that anyone involved in data reduction and classification should be familiar with: contour search, interval splines, chain encoding, the effects of digitizing the input data, pattern representation and classification, etc.

Get it. You'll be glad you did.

-Drew
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Amazon.com: 4.5 von 5 Sternen  2 Rezensionen
28 von 28 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen From 1973 until recently this was the classic text on pattern recognition 13. Februar 2008
Von Michael R. Chernick - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
This book was published in 1973 and there have been many advances since. Still I find it provides great exposition of the fundamental concepts. In fact the nearest neighbor algorithms that are now popular are covered in this book and date back to the work of Cover and Hart in the late 1960s. Those new to pattern recognition who think kth nearest neighbor rules are new should read this book to find out exactly when it was really thought up.
For a more up-to-date treatment, see McLachlan's recent book in the Wiley statistics series. However, this book provides valuable explanations of Bayes rules and shows pictorially what the boundaries look like for linear and quadratic classifiers. In fact I borrowed their pictures in Chapter 2 of my book on bootstrap methods and it appears in both the first and second editions of my book.

The authors with the help of a third author have updated this book recently and I highly recommend the new addition which maintains many of the nice features of the original.
9 von 14 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Gotta have it 10. Februar 1997
Von Ein Kunde - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
While it's becoming a little dated (no problem, v.2 is in the works), this is the "must have" book for anyone concerned with pattern classification and scene analysis - not just computer vision, but anything related to pattern classification. It introduces all of the basic techniques and approaches that anyone involved in data reduction and classification should be familiar with: contour search, interval splines, chain encoding, the effects of digitizing the input data, pattern representation and classification, etc.

Get it. You'll be glad you did.

-Drew
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