Product Description

"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; and, 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.

Product Details

• Amazon Sales Rank: #72817 in Books
• Published on: 2008-09-24
• Original language: English
• Number of items: 1
• Binding: Paperback
• 555 pages

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.

Customer Reviews

So close to easy
The book encourages early attempts at jumping in; however, this is a mistake as the early examples simply are not complete and they won't work.
However, persevere with the book and the OpenCV free library, and you will be richly rewarded.
You simply must make extensive use of the index to dig up the information necessary for completion of the first examples, and perseverence will leave you with a basic test structure into which you can plug the many image processing functions with minimal changes.
This is fun.
Frankly I haven't got the video working yet, and frankly I've been more interested in the static processing since my job needs this more.
I've got the libraries working on both Linux and XP, with splendid, visually impressive, results.
When I get more time I'll be working through to the advanced examples.
Meanwhile, I recommend recent versions of KDevelop-c/c++ if you are working on Linux; the Library dependencies are even easier than windows.

OpenCV
This is the standard book covering the OpenCV library - Open Computer Vision.
A boon is it avoids getting too much into the maths, and has many practical, basic, coding examples explaining the various facilities available in the library. Tracking; Segmentation, and various forms of image and video analysis.
The book is excellent for getting you started with coding your ideas.

Good Book
It is a very good introduction on OpenCV book but it does not cover every library used in OpenCV. So, you still need a lot helps from Internet.