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
What Is OpenCV?
Who Uses OpenCV?
What Is Computer Vision?
The Origin of OpenCV
Downloading and Installing OpenCV
Getting the Latest OpenCV via Git
More OpenCV Documentation
OpenCV Contribution Repository
Chapter 2Introduction to OpenCV
First Program—Display a Picture
A Simple Transformation
A Not-So-Simple Transformation
Input from a Camera
Writing to an AVI File
Chapter 3Getting to Know OpenCV Data Types
OpenCV Data Types
Chapter 4Images and Large Array Types
Dynamic and Variable Storage
Chapter 5Array Operations
More Things You Can Do with Arrays
Chapter 6Drawing and Annotating
Chapter 7Functors in OpenCV
Objects That “Do Stuff”
Chapter 8Image, Video, and Data Files
HighGUI: Portable Graphics Toolkit
Working with Image Files
Working with Video
Chapter 9Cross-Platform and Native Windows
Working with Windows
Chapter 10Filters and Convolution
Before We Begin
Derivatives and Gradients
Convolution with an Arbitrary Linear Filter
Chapter 11General Image Transforms
Stretch, Shrink, Warp, and Rotate
Chapter 12Image Analysis
Discrete Fourier Transform
The Canny Edge Detector
Chapter 13Histograms and Templates
Histogram Representation in OpenCV
Basic Manipulations with Histograms
Some More Sophisticated Histograms Methods
More to Do with Contours
Matching Contours and Images
Chapter 15Background Subtraction
Overview of Background Subtraction
Weaknesses of Background Subtraction
Averaging Background Method
A More Advanced Background Subtraction Method
Connected Components for Foreground Cleanup
Comparing Two Background Methods
OpenCV Background Subtraction Encapsulation
Chapter 16Keypoints and Descriptors
Keypoints and the Basics of Tracking
Generalized Keypoints and Descriptors
Concepts in Tracking
Dense Optical Flow
Mean-Shift and Camshift Tracking
Chapter 18Camera Models and Calibration
Putting Calibration All Together
Chapter 19Projection and Three-Dimensional Vision
Affine and Perspective Transformations
Three-Dimensional Pose Estimation
Structure from Motion
Fitting Lines in Two and Three Dimensions
Chapter 20The Basics of Machine Learning in OpenCV
What Is Machine Learning?
Legacy Routines in the ML Library
Chapter 21StatModel: The Standard Model for Learning in OpenCV
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.
The animal on the cover of Learning OpenCV 3 is a giant, or great, peacock moth (Saturnia pyri). Native to Europe, the moth’s range includes southern France and Italy, the Iberian Peninsula, and parts of Siberia and northern Africa. It inhabits open landscapes with scattered trees and shrubs and can often be found in parklands, orchards, and vineyards, where it rests under shade trees during the day.
The largest of the European moths, giant peacock moths have a wingspan of up to six inches; their size and nocturnal nature can lead some observers to mistake them for bats. Their wings are gray and grayish-brown with accents of white and yellow. In the center of each wing, giant peacock moths have a large eyespot, a distinctive pattern most commonly associated with the birds they are named for.
Many of the animals on O'Reilly covers are endangered; all of them are important to the world. To learn more about how you can help, go to animals.oreilly.com.
The cover image is from Cassell’s Natural History, Volume 5.
This is the Best OpenCV 3 book: practical implementation of the best CV book "Digital Image Processing (3rd Edition) by Rafael C. Gonzalez (Author), Richard E. Woods". Must Have if you are working with CV.
Bottom Line Yes, I would recommend this to a friend