- Hardcover: 598 pages
- Publisher: Cambridge University Press; 1 edition (June 18, 2012)
- Language: English
- ISBN-10: 1107011795
- ISBN-13: 978-1107011793
- Product Dimensions: 7 x 1.1 x 10 inches
- Shipping Weight: 3.1 pounds (View shipping rates and policies)
- Average Customer Review: 24 customer reviews
- Amazon Best Sellers Rank: #462,463 in Books (See Top 100 in Books)
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Computer Vision: Models, Learning, and Inference 1st Edition
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"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
"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."
from the Foreword by Andrew Fitzgibbon
"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
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.
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Dr. Zdenek Kalal
But why I can't download the errata from the author's webpage.
Is there any one can help me? e-mail: email@example.com