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

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

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
By 作者: Adrian Kaehler - Gary Bradski
ISBN-10 书号: 1491937998
ISBN-13 书号: 9781491937990
Edition 版本: 1
Release Finelybook 出版日期: 2017-01-08
Pages 页数: 1024

1491937998

The Book Description robot was collected from Amazon and arranged by Finelybook

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

Contents


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


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