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    Computer Vision: Models, Learning, and Inference (Hardback) By (author) Simon J. D. Prince

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    DescriptionThis modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. * Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry * A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking * More than 70 algorithms are described in sufficient detail to implement * More than 350 full-color illustrations amplify the text * The treatment is self-contained, including all of the background mathematics * Additional resources at www.computervisionmodels.com


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  • Full bibliographic data for Computer Vision

    Title
    Computer Vision
    Subtitle
    Models, Learning, and Inference
    Authors and contributors
    By (author) Simon J. D. Prince
    Physical properties
    Format: Hardback
    Number of pages: 598
    Width: 177 mm
    Height: 253 mm
    Thickness: 28 mm
    Weight: 1,400 g
    Language
    English
    ISBN
    ISBN 13: 9781107011793
    ISBN 10: 1107011795
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    Warengruppen-Systematik des deutschen Buchhandels: 16320
    B&T Modifier: Academic Level: 02
    B&T Modifier: Text Format: 06
    B&T General Subject: 229
    B&T Modifier: Text Format: 01
    B&T Merchandise Category: COM
    Ingram Subject Code: XG
    BISAC V2.8: COM012000
    BIC subject category V2: UYQV
    LC subject heading:
    DC22: 006.37, 006.3/7
    BISAC V2.8: COM016000
    LC subject heading: , ,
    DC23: 006.37
    LC classification: TA1634 .P75 2012
    Ingram Theme: ASPT/SCITAS
    Thema V1.0: UG, UYQV, UYQP
    Edition statement
    New ed.
    Illustrations note
    357 colour illus. 5 tables 201 exercises
    Publisher
    CAMBRIDGE UNIVERSITY PRESS
    Imprint name
    CAMBRIDGE UNIVERSITY PRESS
    Publication date
    30 August 2012
    Publication City/Country
    Cambridge
    Author Information
    fm.author_biographical_note1
    Review quote
    'Computer vision and machine learning have married and this book is their child. It gives the machine learning fundamentals you need to participate in current computer vision research. It's really a beautiful book, showing everything clearly and intuitively. I had lots of 'aha!' moments as I read through the book. This is an important book for computer vision researchers and students, and I look forward to teaching from it.' William T. Freeman, Massachusetts Institute of Technology 'With clarity and depth, this book introduces the mathematical foundations of probabilistic models for computer vision, all with well-motivated, concrete examples and applications. Most modern computer vision texts focus on visual tasks; Prince's beautiful new book is natural complement, focusing squarely on fundamental techniques, emphasizing models and associated methods for learning and inference. I think every serious student and researcher will find this book valuable. I've been using draft chapters of this remarkable book in my vision and learning courses for more than two years. It will remain a staple of mine for years to come.' David J. Fleet, University of Toronto 'This book addresses the fundamentals of how we make progress in this challenging and exciting field. I look forward to many decades with [this book] on my shelf, or indeed, I suspect, open on my desktop.' Andrew Fitzgibbon, from the Foreword 'Prince's magnum opus provides a fully probabilistic framework for understanding modern computer vision. With straightforward descriptions, insightful figures, example applications, exercises, background mathematics, and pseudocode, this book is self-contained and has all that is needed to explore this fascinating discipline.' Roberto Cipolla, University of Cambridge 'The author's goal, as stated in the preface, is to provide a book that focuses on the models involved, and I think the book has succeeded in doing that. I learned quite a bit and would recommend this text highly to the motivated, mathematically mature reader.' Jeffrey Putnam, Computing Reviews
    Table of contents
    Part I. Probability: 1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision: 5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models: 9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing: 12. Image preprocessing and feature extraction; Part V. Models for Geometry: 13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision: 16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices: A. Optimization; B. Linear algebra; C. Algorithms.