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Bradski Gary - Learning Opencv 3: Computer Vision in C++ with the Opencv Library

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Bradski Gary Learning Opencv 3: Computer Vision in C++ with the Opencv Library
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Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. Youll learn what it takes to build applications that enable computers to see and make decisions based on that data.

With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what youve learned.

This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.

  • Learn OpenCV data types, array types, and array...
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    Learning OpenCV 3

    by Adrian Kaehler and Gary Bradski

    Copyright 2017 Adrian Kaehler, Gary Bradski. All rights reserved.

    Printed in the United States of America.

    Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

    OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://www.oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

    Editor: Dawn Schanafelt

    Indexer: Ellen Troutman

    Production Editor: Kristen Brown

    Interior Designer: David Futato

    Copyeditor: Rachel Monaghan

    Cover Designer: Karen Montgomery

    Proofreader: James Fraleigh

    Illustrator: Rebecca Demarest

    • December 2016: First Edition
    Revision History for the First Edition
    • 2016-12-09: First Release
    • 2017-01-20: Second Release
    • 2017-12-15: Third Release

    See http://oreilly.com/catalog/errata.csp?isbn=9781491937990 for release details.

    The OReilly logo is a registered trademark of OReilly Media, Inc. Learning OpenCV 3, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

    While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

    978-1-491-93799-0

    [M]

    Preface

    This book provides a working guide to the C++ Open Source Computer Vision Library (OpenCV) version 3.x and gives a general background on the field of computer vision sufficient to help readers use OpenCV effectively.

    Purpose of This Book

    Computer vision is a rapidly growing field largely because of four trends:

    • The advent of mobile phones put millions of cameras in peoples hands.

    • The Internet and search engines aggregated the resulting giant flows of image and video data into huge databases.

    • Computer processing power became a cheap commodity.

    • Vision algorithms themselves became more mature (now with the advent of deep neural networks, which OpenCV is increasingly supporting; see dnn at ]).

    OpenCV has played a role in the growth of computer vision by enabling hundreds of thousands of people to do more productive work in vision. OpenCV 3.x now allows students, researchers, professionals, and entrepreneurs to efficiently implement projects and jump-start research by providing them with a coherent C++ computer vision architecture that is optimized over many platforms.

    The purpose of this book is to:

    • Comprehensively document OpenCV by detailing what function calling conventions really mean and how to use them correctly

    • 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 code examples to start from

    • Suggest ways to fix some of the more advanced routines when something goes wrong

    This book documents OpenCV in a way that allows the reader to rapidly do interesting and fun things in computer vision. It gives an intuitive understanding of how the algorithms work, which serves to guide the reader in designing and debugging vision applications and also makes the formal descriptions of computer vision and machine learning algorithms in other texts easier to comprehend and remember.

    Who This Book Is For

    This book contains descriptions, working code examples, and explanations of the C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be helpful to many different kinds of users:

    Professionals and entrepreneursFor practicing professionals who need to rapidly prototype or professionally implement computer vision systems, the sample code provides a quick framework with which to start. Our descriptions of the algorithms can quickly teach or remind the reader how they work. OpenCV 3.x sits on top of a hardware acceleration layer (HAL) so that implemented algorithms can run efficiently, seamlessly taking advantage of a variety of hardware platforms.StudentsThis 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. Weve found it effective to have students rapidly cover an accessible text while the instructor fills in formal exposition where needed and supplements that with current papers or guest lectures from experts. The students can meanwhile start class projects earlier and attempt more ambitious tasks.HobbyistsComputer vision is funheres 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.

    What This Book Is Not

    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 clarify 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 has more of an applied nature. It 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, we believe that by reading the explanations here first, a student will not only learn the theory better, but remember it longer as well. 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.

    About the Programs in This Book

    All the program examples in this book are based on OpenCV version 3.x. The code should work under Linux, Windows, and OS X. Using references online, OpenCV 3.x has full support to run on Android and iOS. Source code for the examples in the book can be fetched from this books website; source code for OpenCV is available on GitHub; and prebuilt versions of OpenCV can be loaded from its SourceForge site.

    OpenCV is under ongoing development, with official releases occurring quarterly. To stay completely current, you should obtain your code updates from the aforementioned GitHub site. OpenCV maintains a website at http://opencv.org; for developers, there is a wiki at https://github.com/opencv/opencv/wiki.

    Prerequisites

    For the most part, readers need only know how to program in C++. Many of the math sections in this book are optional and are labeled as such. The mathematics involve simple algebra and basic matrix algebra, and assume 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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