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

Learning OpenCV 3: Computer Vision in C++ with the OpenCV LibraryReviews
Author: Adrian Kaehler, Gary Bradski
Pub Date: 2017
ISBN: 978-1-491-93799-0
Pages: 1024
Language: English
Format: PDF/EPUB
Size: 84 Mb

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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
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Table of Contents

1. Overview
2. Introduction to OpenCV
3. Getting to Know OpenCV Data Types
4. Images and Large Array Types
5. Array Operations
6. Drawing and Annotating
7. Functors in OpenCV
8. Image, Video, and Data Files
9. Cross-Platform and Native Windows
10. Filters and Convolution
11. General Image Transforms
12. Image Analysis
13. Histograms and Templates
14. Contours
15. Background Subtraction
16. Keypoints and Descriptors
17. Tracking
18. Camera Models and Calibration
19. Projection and Three-Dimensional Vision
20. The Basics of Machine Learning in OpenCV
21. StatModel: The Standard Model for Learning in OpenCV
22. Object Detection
23. Future of OpenCV
A. Planar Subdivisions
B. opencv_contrib
C. Calibration Patterns