"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
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.
Dr. Gary Rost Bradski is a consulting professor in the CS department at Stanford University AI Lab where he mentors robotics, machine learning and computer vision research. He is also Senior Scientist at Willow Garage http://www.willowgarage.com, a recently founded robotics research institute/incubator. He has a BS degree in EECS from U.C. Berkeley and a PhD from Boston University. He has 20 years of industrial experience applying machine learning and computer vision spanning option trading operations at First Union National Bank, to computer vision at Intel Research to machine learning in Intel Manufacturing and several startup companies in between. Gary started the Open Source Computer Vision Library (OpenCV http://sourceforge.net/projects/opencvlibrary/ ), the statistical Machine Learning Library (MLL comes with OpenCV), and the Probabilistic Network Library (PNL). OpenCV is used around the world in research, government and commercially. The vision libraries helped develop a notable part of the commercial Intel performance primitives library (IPP http://tinyurl.com/36ua5s). Gary also organized the vision team for Stanley, the Stanford robot that won the DARPA Grand Challenge autonomous race across the desert for a $2M team prize and helped found the Stanford AI Robotics project at Stanford http://www.cs.stanford.edu/group/stair/ working with Professor Andrew Ng. Gary has over 50 publications and 13 issued patents with 18 pending. He lives in Palo Alto with his wife and 3 daughters and bikes road or mountains as much as he can.
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.
The image on the cover of Learning OpenCV is a giant, or great, peacock moth (Saturnia pyri). Native to Europe, the moth's range includes southern France and Italy, the Iberian Peninsula, and parts of Siberia and northern Africa. It inhabits open landscapes with scattered trees and shrubs and can often be found in parklands, orchards, and vineyards, where it rests under shade trees during the day.
The largest of the European moths, giant peacock moths have a wingspan of up to sixinches; their size and nocturnal nature can lead some observers to mistake them forbats. Their wings are gray and grayish-brown with accents of white and yellow. In thecenter of each wing, giant peacock moths have a large eyespot, a distinctive pattern mostcommonly associated with the birds they are named for.