- Hardcover: 598 pages
- Publisher: Cambridge University Press (18 Jun. 2012)
- Language: English
- ISBN-10: 1107011795
- ISBN-13: 978-1107011793
- Product Dimensions: 17.7 x 2.8 x 25.3 cm
- Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)
- Amazon Bestsellers Rank: 372,262 in Books (See Top 100 in Books)
- See Complete Table of Contents
Computer Vision: Models, Learning, and Inference Hardcover – 18 Jun 2012
|New from||Used from|
- Choose from over 13,000 locations across the UK
- Prime members get unlimited deliveries at no additional cost
- Find your preferred location and add it to your address book
- Dispatch to this address when you check out
Frequently Bought Together
Customers Who Bought This Item Also Bought
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your e-mail address or mobile phone number.
'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
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.See all Product Description
What Other Items Do Customers Buy After Viewing This Item?
Top Customer Reviews
Most Helpful Customer Reviews on Amazon.com (beta)
1) Everything is explained in terms of (essentially) the same probabilistic models. That probably doesn't sound seriously exciting, but imagine the alternative of having to learn all the complicated math for doing object recognition, camera pose estimation, tracking, pose regression, shape modeling etc, but each one using ITS OWN notation and completely different "slices" of applied machine learning! It was hard to learn, and very hard to teach. Here, almost everything is consistent (even Structure from Motion is somehow made to fit the same notation). So if you can survive Chapters 2-4 (spread gently over ~40 pages), you'll likely absorb the rest without the usual agony.
2) On the book's website, Prince has built a collection of slides (pretty plain, but good), and an AMAZING (still evolving?) 75-page booklet of algorithms. While the textbook is accurate, there's normally quite some head-scratching to turn the equations into code. You obviously still have to write the code yourself, but now you have a recipe! It's clear the book would be unreadable if each algorithm's details had been included in the main text, so this seems like an ok compromise. This really could be the next "Numerical Recipes in C," but for vision :) There are interesting links to other people's data and code online too, and solutions to some of the problem sets.
My one request would be for the Algorithm booklet to be part of (or just link to) a Matlab-Central-like forum, where people could help each other work through the implementation details, and suggest improvements and tricks (for different problem domains or when the data is too big for memory). When computer-savvy biologists etc. need help with some automated-monitoring project, I sometimes hand them a vision research paper, and point them to the relevant chapter in this book to better understand it.
This book has been instrumental in helping me though a very grueling masters course in Machine Learning at University College London(UCL). It has been responsible for me developing a passion for Vision as it gave me the courage to explore the state-of-the art algorithms and try to make them better. A definite buy for someone looking to do Vision but lacks the knowledge and confidence in dealing with the mathematics behind it.
Dr. Zdenek Kalal
Just one example, Simon's explanation of the Expectation-Maximization algorithm in which he shows in two nice graphs how the algorithm step-wise improves its estimation. Brilliant!
I'm a critical reviewer, but this author knows how to combine in-depth exposition with proper visualizations. He is not just having some exposition of the material at hand, he has the reader in mind, and tries to answer questions that pop up in your head. His emphasis on machine learning makes it also a very coherent book. You'll enjoy it!