Computer Vision: Models, Learning, and InferenceSimon J.D. Prince
A new machine vision textbook with 600 pages, 359 colour figures, 201 exercises and 1060 associated Powerpoint slides
Published by Cambridge University Press
NOW AVAILABLE from Amazon and other booksellers.
"Simon Prince’s wonderful book presents a principled model-based approach to computer vision that unifies disparate algorithms, approaches, and topics under the guiding principles of probabilistic models, learning, and efficient inference algorithms. A deep understanding of this approach is essential to anyone seriously wishing to master the fundamentals of computer vision and to produce state-of-the art results on real-world problems. I highly recommend this book to both beginning and seasoned students and practitioners as an indispensable guide to the mathematics and models that underlie modern approaches to computer vision."
Richard Szeliski, Microsoft Research
"Computer vision and machine learning have gotten 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
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