Learning OpenCV3 Computer Vision in C++ with the OpenCV Library.pdf

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. Title: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library Author: Adrian Kaehler, Gary Bradski Length: 1024 pages Edition: 1 Language: English Publisher: O'Reilly Media Publication Date: 2017-01-08 ISBN-10: 1491937998 ISBN-13: 9781491937990 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 Table of 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 Description, Learning OpenCV puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of OpenCV, the widely used free open-source library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to 'see' and make decisions based on the data. With this book, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications., Full Description, Learning OpenCV puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of OpenCV, the widely used free open-source library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to 'see' and make decisions based on the data., Computer vision is everywhere -- in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It helps robot cars drive by themselves, stitches Google maps and Google Earth together, checks the pixels on your laptop's LCD screen, and makes sure the stitches in your shirt are OK., OpenCV provides an easy-to-use computer vision infrastructure along with a comprehensive library containing more than 500 functions that can run vision code in real time. With Learning OpenCV, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications., The book includes:, * A thorough introduction to OpenCV, * Getting input from cameras, * Transforming images, * Shape matching, * Pattern recognition, including face detection, * Segmenting images, * Tracking and motion in 2 and 3 dimensions, * Machine learning algorithms, Hands-on exercises at the end of each chapter help you absorb the concepts, and an appendix explains how to set up an OpenCV project in Visual Studio. OpenCV is written in performance optimized C/C++ code, runs on Windows, Linux, and Mac OS X, and is free for commercial and research use under a BSD license., Getting machines to see is a challenging but entertaining goal. If you're intrigued by the possibilities, Learning OpenCV gets you started on building computer vision applications of your own.