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《学习0penCV》将你置身于迅速发展的计算机视觉领域。本书作者是免费开源0penCV的发起人，这本书为你介绍了计算机视觉，例证了如何迅速建立使计算机能“看”的应用程序，以及如何基于计算机获取的数据作出决策。

计算机视觉几乎随处可见：安全系统、管理检验系统、医学图像分析、无人机等。它将Google地图和Google地球结合在一起，在LCD屏幕上核对像素，确保衬衫上的每一个针脚都完全缝合。OpenCV提供了一个简易实用的计算机视觉框架以及一个含有超过500种可以实时运行视觉代码的函数的综合库。《学习OpenCV》在每一章里教授任何OpenCV的开发者或热爱者如何在这些实战经验的帮助下迅速掌握该软件。这本书包括了如下内容：

对0penCV全面详尽的介绍

从照相机中导入图片

转换图像

分割图像和形状匹配

模式识别，包括人脸检测

两到三个计量单位间的跟踪和运动

立体视觉中的3D再现

机器学习算法

使机器能看见是一个具有挑战却又充满乐趣的目标。无论你是想建立一个简单的还是复杂的视觉应用程序，《学习OpenCV》都是你入门的教材。

Preface

1. 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

2. 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

3. Gettingto KnowOpenCV

OpenCV Primitive Data Types

CvMat Matrix Structure

IplImage Data Structure

Matrix and Image Operators

Drawing Things

Data Persistence

Integrated Performance Primitives

Summary

Exercises

4. HighGUI

A Portable Graphics Toolkit

Creating a Window

Loading an Image

Displaying Images

Working with Video

ConvertImage

Exercises

5. Image Processing

Overview

Smoothing

Image Morphology

Flood Fill

Resize

Image Pyramids

Threshold

Exercises

6. 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

7. Histograms and Matching

Basic Histogram Data Structure

Accessing Histograms

Basic Manipulations with Histograms

Some More Complicated Stuff

Exercises

8. Contours

Memory Storage

Sequences

Contour Finding

Another Contour Example

More to Do with Contours

Matching Contours

Exercises

9. Image Parts and Segmentation

Parts and Segments

Background Subtraction

Watershed Algorithm

Image Repair by Inpainting

Mean-Shift Segmentation

Delaunay Triangulation, Voronoi Tesselation

Exercises

10. Tracking and Motion

The Basics of Tracking

Corner Finding

Subpixel Corners

Invariant Features

Optical Flow

Mean-Shift and Camshift Tracking

Motion Templates

Estimators

The Condensation Algorithm

Exercises

11. Camera Models and Calibration

Camera Model

Calibration

Undistortion

Putting Calibration All Together

Rodrigues Transform

Exercises

12. Projection and 3D Vision

Projections

Affine and Perspective Transformations

POSIT: 3D Pose Estimation

Stereo Imaging

Structure from Motion

Fitting Lines in Two and Three Dimensions

Exercises

13. Machine Learning

What Is Machine Learning

Common Routines in the ML Library

Mahalanobis Distance

K-Means

Naive/Normal Bayes Classifier

Binary Decision Trees

Boosting

　Random Trees

　Face Detection or Haar Classifier

　Other Machine Learning Algorithms

　Exercises

14. OpenCV's Future

Past and Future

　Directions

　OpenCV for Artists

　Afterword

　Bibliography

Index

伦比亚大学计算机科学教授Gary Rost BradSki博士，斯坦福大学人工智能实验室的顾问教授，也是Willow Garage公司机器；人学研究协会的资深科学家。Ad rian Kaehler博士，Applied Mind S公司的资深科学家，从事机器学习、统计建模、计算机视觉和机器人学方面的研究。

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Chapter 1 oVerviw

　　What Is OpenCV?

　　OpenCV [OpenCV] is an open source (see http://opensource.org) computer vision library available from http://SourceForge.net/projects/opencvlibrary. The library is written in Cand C++ and runs under Linux, Windows and Mac OS X. There is active development on interfaces for Python, Ruby, Matlab, and other languages.

OpenCV was designed for computational efficiency and with a strong focus on realtime applications. OpenCV is written in optimized C and can take advantage of mul ticore processors. If you desire further automatic optimization on Intel architectures [Intel], you can buy Intel's Integrated Performance Primitives (IPP) libraries [IPP], which consist of low-level optimized routines in many different algorithmic areas. OpenCV automatically uses the appropriate IPP library at runtime if that library is installed.

One of OpenCV's goals is to provide a simple-to-use computer vision infrastructure that helps people build fairly sophisticated vision applicatio'ns quickly. The OpenCV library contains over 500 functions that span many areas in vision。 including factory product inspection, medical imaging, security, user interface, camera calibration, stereo vision, and robotics. Because computer vision and machine learning often go hand-inhand, OpenCV also contains a full, general-purpose Machine Learning Library (MLL).This sublibrary is focused on statistical pattern recognition and clustering. The MLL is highly useful for the vision tasks that are at the core of OpenCV's mission, but it is gen eral enough to be used for any machine learning problem.

Who Uses OpenCV?

Most computer scientists and practical programmers are aware of some facet of the role that computer vision plays. But few people are aware of all the ways in which computer vision is used. For example, most people are somewhat aware of its use in surveillance,and many also know that it is increasingly being used for images and yideo on the Web. A few have seen some use of computer vision in game interfaces.

……

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Computer vision has a rich future ahead, and it seems likely to be one of the key enabling technologies for the 21st century. Likewise, OpenCV seems likely to be (at least in part) one of the key enabling technologies for computer vision. Endless opportunities for creativity and profound contribution lie ahead. We hope that this book encourages, excites, and enables all who are interested in joining the vibrant computer vision community.

OpenCV倾向于支持鉴别式算法，而不倾向于产生式算法。虽然这两者的区别不是非常清晰，但是鉴别式模型在根据给定的数据做出预测上有优势，而产生式模型则是在为你提供更强大的数据表达或者有条件地生成新数据时有优势。

These sequences are sequences of points; more precisely, they are contours—the actual topic of this chapter. The key thing to remember about contours is that they are just a special case of sequences.

writing efficient code is often very difficult to do in a modular way

编辑推荐

“这本宝库对专业人员来讲非常有用，对初涉这个领域的人们也是个绝好的工具。像其宣传的那样，它是一组计算机视觉算法。”

　　 ——William T．Freeman．

麻省理工学院计算机科学与人工智能实验室“《学习0penCV》即将占据每从从事计算机视觉领域的人的书架上*显著的一处。”

　　——David Lowe，英属哥

书摘插图

书籍介绍

《学习OpenCV(影印版)》将你置身于迅速发展的计算机视觉领域。《学习OpenCV(影印版)》作者是免费开源0penCV的发起人，《学习OpenCV(影印版)》为你介绍了计算机视觉，例证了如何迅速建立使计算机能“看”的应用程序，以及如何基于计算机获取的数据作出决策。计算机视觉几乎随处可见：安全系统、管理检验系统、医学图像分析、无人机等。它将Google地图和Google地球结合在一起，在LCD屏幕上核对像素，确保衬衫上的每一个针脚都完全缝合。OpenCV提供了一个简易实用的计算机视觉框架以及一个含有超过500种可以实时运行视觉代码的函数的综合库。《学习OpenCV》在每一章里教授任何OpenCV的开发者或热爱者如何在这些实战经验的帮助下迅速掌握该软件。《学习OpenCV(影印版)》包括了如下内容：

对0penCV全面详尽的介绍

从照相机中导入图片

转换图像

分割图像和形状匹配

模式识别，包括人脸检测

两到三个计量单位间的跟踪和运动

立体视觉中的3D再现

机器学习算法

使机器能看见是一个具有挑战却又充满乐趣的目标。无论你是想建立一个简单的还是复杂的视觉应用程序，《学习OpenCV》都是你入门的必备教材。