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从零开始学C++程序设计

作者:吴惠茹

出版社:机械工业出版社

ISBN:9787111564560

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

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

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