Computer Vision: Models, Learning, and Inference

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Simon J. D. Prince, University College London

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Publisher:

Cambridge University Press

Publication date:

05 August 2012

18 June 2012

ISBN:

9780511996504

9781107011793

DOI:

https://doi.org/10.1017/CBO9780511996504

Dimensions:

(253 x 177 mm)

Weight & Pages:

1.4kg, 598 Pages

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Subjects:: Engineering, Computer Science, Computer Graphics, Image Processing and Robotics, Image Processing and Machine Vision

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Subjects:: Engineering, Computer Science, Computer Graphics, Image Processing and Robotics, Image Processing and Machine Vision

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This 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 geometryA unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object trackingMore than 70 algorithms are described in sufficient detail to implementMore than 350 full-color illustrations amplify the textThe treatment is self-contained, including all of the background mathematicsAdditional resources at www.computervisionmodels.com

‘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 Source: Computing Reviews

Computer Vision is an excellent book for most imaging-related fields.’

Guiren Wang - University of South Carolina

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Computer Vision

Models, Learning, and Inference

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