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Learning OpenCV: Computer Vision with the OpenCV Libraryby Gary Bradski
Synopses & Reviews
"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:
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.
Book News Annotation:
Bradski (computer science, Stanford U.) has teamed with Kaehler, a senior computer scientist specializing in machine learning and statistical modeling, to offer this tutorial on how the OpenCV software library is used in the rapidly expanding field of computer vision. The authors show web developers and IT professionals how this framework is used to construct such applications as Google Maps and Google Earth and how OpenCV monitors pixels on LCD screens and runs vision code in real time. Hands-on exercises in each chapter offer detailed descriptions on getting input from cameras, segmenting images and shape matching, 3D reconstruction from stereo vision and machine learning algorithms. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)
The authors of "OpenCV" explain how to put computer vision to work. They bring readers up to speed with the latest updates in the field and show them how to program with existing free code.
About the Author
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.
Table of Contents
PrefaceChapter 1: OverviewChapter 2: Introduction to OpenCVChapter 3: Getting to Know OpenCVChapter 4: HighGUIChapter 5: Image ProcessingChapter 6: Image TransformsChapter 7: Histograms and MatchingChapter 8: ContoursChapter 9: Image Parts and SegmentationChapter 10: Tracking and MotionChapter 11: Camera Models and CalibrationChapter 12: Projection and 3D VisionChapter 13: Machine LearningChapter 14: OpenCV's FutureChapter 15: BibliographyColophon
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