Learning OpenCV : Computer Vision with the OpenCV Library
Bradski, Gary, Kaehler, Adrian
Language: English
Published by O'Reilly Media, Incorporated, 2008
- Softcover
- Used
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- Title
- Learning OpenCV : Computer Vision with the OpenCV Library
- Author
- Bradski, Gary, Kaehler, Adrian
- Publisher
- O'Reilly Media, Incorporated
- Publication year
- 2008
- Condition
- Very Good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0596516134
- ISBN 13
- 9780596516130
- Item weight
- 2.165 pounds
- Dimensions
- N/A
Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV 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 that data.
Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.
Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:
- A thorough introduction to OpenCV
- Getting input from cameras
- Transforming images
- Segmenting images and shape matching
- Pattern recognition, including face detection
- Tracking and motion in 2 and 3 dimensions
- 3D reconstruction from stereo vision
- Machine learning algorithms
Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Adrian Kaehler is a senior scientist at Applied Minds Corporation. His current research includes topics in machine learning, statistical modeling, computer vision and robotics. Adrian received his Ph.D. in Theoretical Physics from Columbia university in 1998. Adrian has since held positions at Intel Corporation and the Stanford University AI Lab, and was a member of the winning Stanley race team in the DARPA Grand Challenge. He has a variety of published papers and patents in physics, electrical engineering, computer science, and robotics.
"About the title" may belong to another edition of this title.
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