Kaehler Adrian - Learning OpenCV: [computer vision with the OpenCV library]
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- Book:Learning OpenCV: [computer vision with the OpenCV library]
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Learning OpenCV puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of OpenCV, the widely used free open-source library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to see and make decisions based on the data. With this book, any developer or hobbyist can get up and running with the framework quickly, whether its to build simple or sophisticated vision applications.
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Copyright 2008 Gary Bradski and Adrian Kaehler
O'Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles ( .
Nutshell Handbook, the Nutshell Handbook logo, and the O'Reilly logo are registered trademarks of O'Reilly Media, Inc. Learning OpenCV , the image of a giant peacock moth, and related trade dress are trademarks of O'Reilly Media, Inc.
Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O'Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps.
While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.
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Supplemental files and examples for this book can be found at http://examples.oreilly.com/9780596516130/. Please use a standard desktop web browser to access these files, as they may not be accessible from all ereader devices.
All code files or examples referenced in the book will be available online. For physical books that ship with an accompanying disc, whenever possible, weve posted all CD/DVD content. Note that while we provide as much of the media content as we are able via free download, we are sometimes limited by licensing restrictions. Please direct any questions or concerns to .
This book provides a working guide to the Open Source Computer Vision Library (OpenCV) and also provides a general background to the field of computer vision sufficient to use OpenCV effectively.
Computer vision is a rapidly growing field, partly as a result of both cheaper and more capable cameras, partly because of affordable processing power, and partly because vision algorithms are starting to mature. OpenCV itself has played a role in the growth of computer vision by enabling thousands of people to do more productive work in vision. With its focus on real-time vision, OpenCV helps students and professionals efficiently implement projects and jump-start research by providing them with a computer vision and machine learning infrastructure that was previously available only in a few mature research labs. The purpose of this text is to:
Better document OpenCVdetail what function calling conventions really mean and how to use them correctly.
Rapidly give the reader an intuitive understanding of how the vision algorithms work.
Give the reader some sense of what algorithm to use and when to use it.
Give the reader a boost in implementing computer vision and machine learning algorithms by providing many working coded examples to start from.
Provide intuitions about how to fix some of the more advanced routines when something goes wrong.
Simply put, this is the text the authors wished we had in school and the coding reference book we wished we had at work.
This book documents a tool kit, OpenCV, that allows the reader to do interesting and fun things rapidly in computer vision. It gives an intuitive understanding as to how the algorithms work, which serves to guide the reader in designing and debugging vision applications and also to make the formal descriptions of computer vision and machine learning algorithms in other texts easier to comprehend and remember.
After all, it is easier to understand complex algorithms and their associated math when you start with an intuitive grasp of how those algorithms work.
This book contains descriptions, working coded examples, and explanations of the computer vision tools contained in the OpenCV library. As such, it should be helpful to many different kinds of users.
For those practicing professionals who need to rapidly implement computer vision systems, the sample code provides a quick framework with which to start. Our descriptions of the intuitions behind the algorithms can quickly teach or remind the reader how they work.
StudentsAs we said, this is the text we wish had back in school. The intuitive explanations, detailed documentation, and sample code will allow you to boot up faster in computer vision, work on more interesting class projects, and ultimately contribute new research to the field.
TeachersComputer vision is a fast-moving field. We've found it effective to have the students rapidly cover an accessible text while the instructor fills in formal exposition where needed and supplements with current papers or guest lectures from experts. The students can meanwhile start class projects earlier and attempt more ambitious tasks.
HobbyistsComputer vision is fun, here's how to hack it.
We have a strong focus on giving readers enough intuition, documentation, and working code to enable rapid implementation of real-time vision applications.
This book is not a formal text. We do go into mathematical detail at various points,[] but it is all in the service of developing deeper intuitions behind the algorithms or to make clear the implications of any assumptions built into those algorithms. We have not attempted a formal mathematical exposition here and might even incur some wrath along the way from those who do write formal expositions.
This book is not for theoreticians because it has more of an "applied" nature. The book will certainly be of general help, but is not aimed at any of the specialized niches in computer vision (e.g., medical imaging or remote sensing analysis).
That said, it is the belief of the authors that having read the explanations here first, a student will not only learn the theory better but remember it longer. Therefore, this book would make a good adjunct text to a theoretical course and would be a great text for an introductory or project-centric course.
[] Always with a warning to more casual users that they may skip such sections.
All the program examples in this book are based on OpenCV version 2.0. The code should definitely work under Linux or Windows and probably under OS-X, too. Source code for the examples in the book can be fetched from this book's website (http://www.oreilly.com/catalog/9780596516130). OpenCV can be loaded from its source forge site (http://sourceforge.net/projects/opencvlibrary).
OpenCV is under ongoing development, with official releases occurring once or twice a year. As a rule of thumb, you should obtain your code updates from the source forge CVS server (http://sourceforge.net/cvs/?group_id=22870).
For the most part, readers need only know how to program in C and perhaps some C++. Many of the math sections are optional and are labeled as such. The mathematics involves simple algebra and basic matrix algebra, and it assumes some familiarity with solution methods to least-squares optimization problems as well as some basic knowledge of Gaussian distributions, Bayes' law, and derivatives of simple functions.
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