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Book Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

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    Available in PDF - DJVU Format | Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library.pdf | Language: ENGLISH
    Adrian Kaehler(Author),Gary Bradski(Author)

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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. You’ll 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 you’ve 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 operations
  • Capture and store still and video images with HighGUI
  • Transform images to stretch, shrink, warp, remap, and repair
  • Explore pattern recognition, including face detection
  • Track objects and motion through the visual field
  • Reconstruct 3D images from stereo vision
  • Discover basic and advanced machine learning techniques in OpenCV

Software That Sees Dr. Adrian Kaehler is a senior scientist at Applied Minds Corporation. His current research includes topics in machine learning, statistical modeling, computer vision and robotics. Adrian received his Ph.D. in Theoretical Physics from Columbia university in 1998. Adrian has since held positions at Intel Corporation and the Stanford University AI Lab, and was a member of the winning Stanley race team in the DARPA Grand Challenge. He has a variety of published papers and patents in physics, electrical engineering, computer science, and robotics.Dr. Gary Rost Bradski is a consulting professor in the CS department at Stanford University AI Lab where he mentors robotics, machine learning and computer vision research. He is also Senior Scientist at Willow Garage http://www.willowgarage.com, a recently founded robotics research institute/incubator. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University. He has 20 years of industrial experience applying machine learning and computer vision spanning option trading operations at First Union National Bank, to computer vision at Intel Research to machine learning in Intel Manufacturing and several startup companies in between. Gary started the Open Source Computer Vision Library (OpenCV http://sourceforge.net/projects/​opencvlibrary/ ), the statistical Machine Learning Library (MLL comes with OpenCV), and the Probabilistic Network Library (PNL). OpenCV is used around the world in research, government and commercially. The vision libraries helped develop a notable part of the commercial Intel performance primitives library (IPP http://tinyurl.com/36ua5s). Gary also organized the vision team for Stanley, the Stanford robot that won the DARPA Grand Challenge autonomous race across the desert for a $2M team prize and helped found the Stanford AI Robotics project at Stanford http://www.cs.stanford.edu/group/stair/ working with Professor Andrew Ng. Gary has over 50 publications and 13 issued patents with 18 pending. He lives in Palo Alto with his wife and 3 daughters and bikes road or mountains as much as he can.

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Review Text

  • By Sean E. Kelleher on February 27, 2018

    With this book, I was able to write code to calibrate a pair of stereo webcams, undistort and rectify the images, perform stereo block matching, produce a disparity map, and turn that into a 3D point cloud (I used Point Cloud Library to actually visualize the points). Not too shabby.I have read only a fraction of the book so far. I just jumped in, with the explanation of camera calibration, and went from there.The book does a fairly good job explaining a classic computer vision pipeline, which OpenCV functions are useful at each stage, what the different parameters do, and provides a little bit of primer / math for those want to know "how it works." The book could be considered equal parts cookbook and "introduction to computer vision," with a healthy dose of commentary and foot-notes from the authors (it's not a terse book).The book is a good starting point, and good for rapid prototyping. You WILL need to read papers, online tutorials, and sample code to make use of OpenCV. But the book is better than the online docs, and well worth the price.

  • By Henry Leinhos on March 3, 2018

    Never received my copy. I've read through borrowed copies of this book and it is a great update from the previous 2.4-based book.

  • By Alan Alfredo Osuna Ocegueda on November 28, 2017

    The book is great, easy to understand, good examples, etc.; I seriously don't understand the bad reviews.

  • By Guest on January 12, 2017

    Finally got it after waiting several years. Great book about the latest OpenCV 3

  • By Guest on February 16, 2017

    Really good book, wrote by the opencv author himself!

  • By Andrew Ribeiro on March 12, 2017

    Terribly printed. Code is just thrown in plain text without any distinction. Super lazy. I don't know why we have to make a book that is mostly printed code these days... just make a github repo.

  • By Chris Edwards on June 5, 2017

    What's with the bad reviews for this book? It makes no sense. I'm about half way through this book and it is the first time I've found comprehensive, coherent, and sensible explanations about modern OpenCV. I read the previous ORA OpenCV book and it was ok, but it was more abstract. This one covers the C++ API. What do the functions do and how do they do it. The rationale for all objects, and data types are explained. Vision in general is covered well enough for anyone who thinks they're going to be writing C++ code. I've been reading this on-line with safari and I don't see any typesetting issues. The code may have errors, but it isn't a software project; this is a book that explains why things are the way they are. The how things are is a bonus and mostly looks intact. I think this book will make a fine reference as well as a guide.I'm finding this book an invaluable resource. An island of sanity and stability in a quagmire of different styles and versions and goals that is the OpenCV project. OpenCV is amazing and technically brilliant, but no one ever accused it of having documentation that was too good. This book goes a long way to make up for that.

  • By Love to Read on January 15, 2017

    THE BEST book on computer vision and must read for a beginner or an experienced CV specialist. In addition to describing the features of the OpenCV library, this book does an excellent job at introducing the basic theory behind these functions. For this reason, I give the book 5 stars, because it not only explains OpenCV, it also teaches Computer Vision in a clear and readable way.


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