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Learning OpenCV
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Learning OpenCV puts you right in the middle of the rapidly expanding field
of computer vision. Written by the creators of OpenCV, the widely used free
open-source 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 the data.
Computer vision is everywhere -- in security systems, manufacturing inspection
systems, medical image analysis, Unmanned Aerial Vehicles, and more. It helps
robot cars drive by themselves, stitches Google maps and Google Earth together,
checks the pixels on your laptop's LCD screen, and makes sure the stitches in
your shirt are OK.
OpenCV provides an easy-to-use computer vision infrastructure along with a
comprehensive library containing more than 500 functions that can run vision
code in real time. With Learning OpenCV, any developer or hobbyist can get up
and running with the framework quickly, whether it's to build simple or sophisticated
vision applications.
The book includes:
• A thorough introduction to OpenCV
• Getting input from cameras
• Transforming images
• Shape matching
• Pattern recognition, including face detection
• Segmenting images
• Tracking and motion in 2 and 3 dimensions
• Machine learning algorithms
Hands-on exercises at the end of each chapter help you absorb the concepts,
and an appendix explains how to set up an OpenCV project in Visual Studio. OpenCV
is written in performance optimized C/C++ code, runs on Windows, Linux, and
Mac OS X, and is free for commercial and research use under a BSD license.
Getting machines to see is a challenging but entertaining goal. If you're intrigued
by the possibilities, Learning OpenCV gets you started on building computer
vision applications of your own.
About the Authors
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. His current interest
is in applying highly scalable statistical models in computer vision and in
continuous machine "learning in clutter" in robotics in general. Some
external tools he started for this are 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
(for example in wide use within Google). All libraries are open, and free on
Source Forge for commercial or research purposes. The vision libraries use and
helped develop a notable part of the commercial Intel performance primitives
library (IPP). Gary led the vision team for Stanley, the Stanford robot that
won the DARPA Grand Challenge autonomous race across the desert for a $2M team
prize. 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,
and computer vision. 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.
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