Learning OpenCV Computer Vision with the OpenCV Library

ISBN-10: 0596516134

ISBN-13: 9780596516130

Edition: 2008 (Revised)

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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 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.
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Book details

List price: $49.99
Copyright year: 2008
Publisher: O'Reilly Media, Incorporated
Publication date: 10/4/2008
Binding: Paperback
Pages: 580
Size: 7.00" wide x 9.25" long x 1.25" tall
Weight: 1.980
Language: English

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 Stanfordrobot 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.

Preface
Purpose
About the Programs in This Book
Prerequisites
How This Book Is Best Used
Conventions Used in This Book
Using Code Examples
Safari�“ Books Online
We'd Like to Hear from You
Acknowledgments
Overview
What Is OpenCV?
Who Uses OpenCV?
What Is Computer Vision?
The Origin of OpenCV
Downloading and Installing OpenCV
Getting the Latest OpenCV via CVS
More OpenCV Documentation
OpenCV Structure and Content
Portability
Exercises
Introduction to OpenCV
Getting Started
First Program—Display a Picture
Second Program—AVI Video
Moving Around
A Simple Transformation
A Not-So-Simple Transformation
Input from a Camera
Writing to an AVI File
Onward
Exercises
Getting to Know OpenCV
OpenCV Primitive Data Types
CvMat Matrix Structure
IplImage Data Structure
Matrix and Image Operators
Drawing Things
Data Persistence
Integrated Performance Primitives
Summary
Exercises
HighGUI
A Portable Graphics Toolkit
Creating a Window
Loading an Image
Displaying Images
Working with Video
ConvertImage
Exercises
Image Processing
Overview
Smoothing
Image Morphology
Flood Fill
Resize
Image Pyramids
Threshold
Exercises
Image Transforms
Overview
Convolution
Gradients and Sobel Derivatives
Laplace
Canny
Hough Transforms
Remap
Stretch, Shrink, Warp, and Rotate
CartToPolar and PolarToCart
LogPolar
Discrete Fourier Transform (DFT)
Discrete Cosine Transform (DCT)
Integral Images
Distance Transform
Histogram Equalization
Exercises
Histograms and Matching
Basic Histogram Data Structure
Accessing Histograms
Basic Manipulations with Histograms
Some More Complicated Stuff
Exercises
Contours
Memory Storage
Sequences
Contour Finding
Another Contour Example
More to Do with Contours spa$$$>
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