Buying Options
Learning OpenCV
Print $49.99
Add to Cart
Print+Ebook $54.99
Add to Cart
Ebook $39.99
(Mobi, PDF, ePub)
Add to Cart
Safari Books Online
Add to Cart
What is this?
Print £38.50
Add to Cart
What is this?

Learning OpenCV

Computer Vision with the OpenCV Library

By
Gary Bradski, Adrian Kaehler
Publisher:
O'Reilly Media
Released:
September 2008
Pages:
576
Press Release
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. With this book, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications.
Full Description
Table of Contents

  1. Chapter 1 Overview

    1. What Is OpenCV?

    2. Who Uses OpenCV?

    3. What Is Computer Vision?

    4. The Origin of OpenCV

    5. Downloading and Installing OpenCV

    6. Getting the Latest OpenCV via CVS

    7. More OpenCV Documentation

    8. OpenCV Structure and Content

    9. Portability

    10. Exercises

  2. Chapter 2 Introduction to OpenCV

    1. Getting Started

    2. First Program—Display a Picture

    3. Second Program—AVI Video

    4. Moving Around

    5. A Simple Transformation

    6. A Not-So-Simple Transformation

    7. Input from a Camera

    8. Writing to an AVI File

    9. Onward

    10. Exercises

  3. Chapter 3 Getting to Know OpenCV

    1. OpenCV Primitive Data Types

    2. CvMat Matrix Structure

    3. IplImage Data Structure

    4. Matrix and Image Operators

    5. Drawing Things

    6. Data Persistence

    7. Integrated Performance Primitives

    8. Summary

    9. Exercises

  4. Chapter 4 HighGUI

    1. A Portable Graphics Toolkit

    2. Creating a Window

    3. Loading an Image

    4. Displaying Images

    5. Working with Video

    6. ConvertImage

    7. Exercises

  5. Chapter 5 Image Processing

    1. Overview

    2. Smoothing

    3. Image Morphology

    4. Flood Fill

    5. Resize

    6. Image Pyramids

    7. Threshold

    8. Exercises

  6. Chapter 6 Image Transforms

    1. Overview

    2. Convolution

    3. Gradients and Sobel Derivatives

    4. Laplace

    5. Canny

    6. Hough Transforms

    7. Remap

    8. Stretch, Shrink, Warp, and Rotate

    9. CartToPolar and PolarToCart

    10. LogPolar

    11. Discrete Fourier Transform (DFT)

    12. Discrete Cosine Transform (DCT)

    13. Integral Images

    14. Distance Transform

    15. Histogram Equalization

    16. Exercises

  7. Chapter 7 Histograms and Matching

    1. Basic Histogram Data Structure

    2. Accessing Histograms

    3. Basic Manipulations with Histograms

    4. Some More Complicated Stuff

    5. Exercises

  8. Chapter 8 Contours

    1. Memory Storage

    2. Sequences

    3. Contour Finding

    4. Another Contour Example

    5. More to Do with Contours

    6. Matching Contours

    7. Exercises

  9. Chapter 9 Image Parts and Segmentation

    1. Parts and Segments

    2. Background Subtraction

    3. Watershed Algorithm

    4. Image Repair by Inpainting

    5. Mean-Shift Segmentation

    6. Delaunay Triangulation, Voronoi Tesselation

    7. Exercises

  10. Chapter 10 Tracking and Motion

    1. The Basics of Tracking

    2. Corner Finding

    3. Subpixel Corners

    4. Invariant Features

    5. Optical Flow

    6. Mean-Shift and Camshift Tracking

    7. Motion Templates

    8. Estimators

    9. The Condensation Algorithm

    10. Exercises

  11. Chapter 11 Camera Models and Calibration

    1. Camera Model

    2. Calibration

    3. Undistortion

    4. Putting Calibration All Together

    5. Rodrigues Transform

    6. Exercises

  12. Chapter 12 Projection and 3D Vision

    1. Projections

    2. Affine and Perspective Transformations

    3. POSIT: 3D Pose Estimation

    4. Stereo Imaging

    5. Structure from Motion

    6. Fitting Lines in Two and Three Dimensions

    7. Exercises

  13. Chapter 13 Machine Learning

    1. What Is Machine Learning

    2. Common Routines in the ML Library

    3. Mahalanobis Distance

    4. K-Means

    5. Naïve/Normal Bayes Classifier

    6. Binary Decision Trees

    7. Boosting

    8. Random Trees

    9. Face Detection or Haar Classifier

    10. Other Machine Learning Algorithms

    11. Exercises

  14. Chapter 14 OpenCV's Future

    1. Past and Future

    2. Directions

    3. OpenCV for Artists

    4. Afterword

  15. Chapter 15 Bibliography

  1. Colophon

View Full Table of Contents
Product Details
Title:
Learning OpenCV
By:
Gary Bradski, Adrian Kaehler
Publisher:
O'Reilly Media
Formats:
  • Print
  • Ebook
  • Safari Books Online
Print Release:
September 2008
Ebook Release:
October 2008
Pages:
576
Print ISBN:
978-0-596-51613-0
| ISBN 10:
0-596-51613-4
Ebook ISBN:
978-0-596-15620-6
| ISBN 10:
0-596-15620-0
Customer Reviews
About the Authors

  1. Gary Bradski

    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.

    View Gary Bradski's full profile page.

  2. Adrian Kaehler

    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.

    View Adrian Kaehler's full profile page.

Colophon

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 six inches; their size and nocturnal nature can lead some observers to mistake them for bats. Their wings are gray and grayish-brown with accents of white and yellow. In the center of each wing, giant peacock moths have a large eyespot, a distinctive pattern most commonly associated with the birds they are named for.

  • Book cover of Learning OpenCV
Got a Question?
icons
Favicon Service and support by Satisfaction