Learning OpenCV : computer vision with the OpenCV library / Gary Bradski and Adrian Kaehler.
Material type:
TextPublisher: Sebastopol, CA : O'Reilly Media, [2017]Copyright date: ©2017Edition: First EditionDescription: xxv, 555 pages : illustrations ; 20 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780596516130Subject(s): Computer vision | C++ (Computer program language)DDC classification: 006.37 | Item type | Current location | Call number | Materials specified | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Book
|
Uofcanada Library | 006.37 GAR (Browse shelf) | Available | 00000802 | |||
|
NB-Book
|
Uofcanada Library | 006.37 GAR/NB (Browse shelf) | Not for loan | 00000738 | |||
|
Book
|
Uofcanada Library | 006.37 GAR (Browse shelf) | Available | 00000739 |
Browsing Uofcanada Library shelves Close shelf browser
|
|
|
|
|
|
|
||
| 006.37 BRA/NB Embedded computer vision / | 006.37 GAR Learning OpenCV : computer vision with the OpenCV library / | 006.37 GAR Learning OpenCV : computer vision with the OpenCV library / | 006.37 GAR/NB Learning OpenCV : computer vision with the OpenCV library / | 006.37 ROB/NB Machine learning for computer vision / | 006.3703 FIS/REF Dictionary of computer vision and image processing / | 006.3823 FRA Genetic algorithms and machine learning for programmers : create AI models and evolve solutions / |
Includes bibliographical references and index.
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.
"This book provides a working guide to the C++ Open Source Computer Vision Library (OpenCV) version 3.x and gives a general background on the field of computer vision sufficient to help readers use OpenCV effectively."--Preface.
"Get started in the rapidly expanding field of computer vision with this practical guide ... 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."--Publisher's website.
There are no comments on this title.