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- Total Pages: 575
- Genre: Computer vision
- Secondary Genre: Object-oriented programming (OOP)
- Illustrations: 1, black & white illustrations
This library is useful for practitioners, and is an excellent tool for those entering the field: it is a set of computer vision algorithms that work as advertised. -William T. Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology 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.
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Dr. Gary Rost Bradski is VP of Technology at Rexee Inc. a new startup applying machine learning to rich media on the web. He is also a consulting professor in the CS department at Stanford University, AI Lab where he mentors robotics, machine learning and computer vision research. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University.Dr. Adrian Kaehler is a senior scientist at Applied Minds Corporation. His current research includes topics in machine learning, statistical modeling, and computer vision. Adrian received his Ph.D. in Theoretical Physics from Columbia university in 1998. Adrian 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.
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