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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 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.
- Sales Rank: #78101 in Books
- Brand: Brand: Cambridge University Press
- Published on: 2012-06-18
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x 1.10" w x 6.97" l, 3.10 pounds
- Binding: Hardcover
- 598 pages
Features
- Used Book in Good Condition
Review
"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
About the Author
Dr Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London. He has taught courses on machine vision, image processing and advanced mathematical methods. He has a diverse background in biological and computing sciences and has published papers across the fields of computer vision, biometrics, psychology, physiology, medical imaging, computer graphics and HCI.
Most helpful customer reviews
24 of 26 people found the following review helpful.
Pretty easy, considering...
By GBrostow
I teach the Machine Vision class at UCL from this textbook (for advanced undergrads + grad students). It's the same class Simon Prince used to teach, so we cover the whole book (ok, skipping a few bits and one whole chapter) in 11 weeks of lectures. The two main reasons I like it are 1) its unified explanation of all the major topics, and 2) the extra materials for students and teachers (free online):
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.
9 of 11 people found the following review helpful.
Great book
By Zdenek Kalal
Computer vision is very active field with increasing number of papers being published every year. While the new papers slowly push the knowledge boundary forward, it is often difficult to separate useful information from noise. At the same time, only a few core principles keep repeating over and over again. This book is absolutely brilliant at presenting these principles and mapping them to the already discovered applications in computer vision. This is a connection that I have not found in any other computer vision book available. A connection that allowed me to better understand my own work and to discover new ways forward. I humbly recommend to buy this book to any person seriously interested in computer vision.
Dr. Zdenek Kalal
TLD Vision
8 of 10 people found the following review helpful.
Voted Book of the Year for Online Resources in 2014
By Let's Compare Options Preptorial
I'm a roboticist and volunteer at Preptorial dot org, the nonprofit test prep service. Prep's 11 million visitors-- far more students than professors-- vote on a variety of texts each year. This outstanding text was voted textbook of the year this year for online resources in technology.
Not only is there a fully searchable pdf of the book on his site, but this "prince" of a professor has included unprecedented MOUNTAINS of additional resources, both intra and extra text, from slides to answers to a complete second text of evolving applied algorithms for the extensive math in the text. I review thousands of texts each year for library picks dot com, mostly in technology and robotics, and I agree with the Preptorial students that I've never seen an author or publisher this generous online, with this level of quality and currency.
In fact, "runners up" with the same level of tools sell for $300 US or more, often charging extra for algo books, student and teacher answer guides, powerpoints, etc. You SHOULD buy this book here on Amazon in addition to the online resources, because you'll need to in order to understand the LaTex of the extensive and detailed math (rather than mice type e-text formulas). On that topic, although the author has gone out of his way to make this accessible to autodidacts, you should use the look inside or website to be sure you can handle the math level. I'd put it at undergrad ONLY with an instructor, even given the online resources, and for self study only if you're grad level or have an extensive probability background (eg. multiple integrals, differential equations, linear algebra).
From a Kindle standpoint, the publisher avoided the problems with ALL e-readers of page breaks between multiple differentials and other formulas with the "tiny type" solution many publishers are now using for e-readers. I personally find a physical text a lot easier to handle than jumping between illustrations and formulas/solutions/ exercises, but that's just me. At least this one isn't slaughtered and unreadable. Frankly, if you can afford the 80 bucks US, go for it. I normally prefer Kindle for savings, but texts of this quality are usually several hundred, so some of us have to go Kindle or web. The "look inside" here uses the Kindle interface to display it on your PC, so check it out if you have concerns before deciding on format.
In the real world of robotic vision, MRI and predictive tracking where I work, numerous solutions don't work (including most recently the Kinect) with quant/qual differential equations, so numeric and stochastic methods have to be used, meaning very intricate probability. Every "small" problem in this field has several texts of info and math needed-- 2D to 3D, for example, let alone predictive tracking, object recognition and projective geometry. The field is becoming so diverse that even astrodynamics - predictive tracking telescopes using magnetic fields, radio and IR are being considered a type of "vision!"
Although computer graphics, in a math sense, is the "inverse" of this field, recognition and projection are closely related, and the author takes the time to relate many fields with the examples, the formulas, and in the online resources, the algorithms that will get you to your goal in MatLab, OpenCV, Python, R, etc. from both 2D to 3D and the reverse. Although polymaths supposedly don't exist any longer, Prince does an amazing job of covering this entire field. When you consider that it now intersects with just about every other science known, from physics and neurochemistry to machine learning, machine vision, image and signal processing and AI to robotics, astronomy and inference/decision theory, that is an amazing feat!
Prince's secret is that he "lifts the veil" on many other journal and online research links for us in addition to the many of his own, so we can keep up to date daily on developments in the field. As one example, if you Bing/Google "USC Computer Vision Bibliography" you'll see one of the links that contains nearly daily updates on research in thousands of aspects of the fields involved. Finally, I'm encouraging Preptorial to BUY this text for students that need financial help, not only for the quality, but to support and encourage the author to keep maintaining and expanding the "recipes" (really functions) that translate formulas into numerical methods into code. Github is nice, but it is contributors like Prince that lay the foundation for those numpy libraries. Another tip for those budget conscious: GNU Octave and R can both be used to model open source what many who have Maple, MatLab, MathCad, etc. do with big budgets. The author's algos translate seamlessly to Python/numpy, octave and R as much as the expensive big boys-- another evidence that this author lives in the real world of struggling students and even self learners and researchers on a budget. Highly recommended.
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