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本书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OpenCV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。

Adrian Kaehlef是一位企业家，同时也是Silicon Valley Deep Learnlng Group的创始人。其工作包括机器学习、统计建模、计算机视觉以及机器人学。他在斯坦福大学AI实验室拥有职位，另外还是DARPA Grand Challerige竞赛获胜团队Stanley的其中一员。

Gary Bradski是Arraiy.ai的CTO，曾在多家创业公司就职，另外还担任斯坦福大学AI实验室计算机科学系的咨询教授。作为OpenCV库的作者，他也是一位广受尊敬的发言人以及开源社区的贡献者。

Preface
1.Overview
What Is OpenCV?
Who Uses OpenCV?
What Is Computer Vision?
The Origin of OpenCV
OpenCV Block Diagram
Speeding Up OpenCV with IPP
Who Owns OpenCV?
Downloading and Installing OpenCV
Installation
Getting the Latest OpenCV via Git
More OpenCV Documentation
Supplied Documentation
Online Documentation and the Wiki
OpenCV Contribution Repository
Downloading and Building Contributed Modules
Portability
Summary
Exercises
2.Introduction to 0penCV
Include Files
Resources
First Program——Display a Picture
Second Program——Video
Moving Around
A Simple Transformation
A Not-So-Simple Transformation
Input from a Camera
Writing to an AVI File
Summary
Exercises
3.Getting to Know OpenCV Data Types
The Basics
OpenCV Data Types
Overview of the Basic Types
Basic Types： Getting Down to Details
Helper Objects
Utility Functions
The Template Structures
Summary
Exercises
4.Images and Large Array Types
Dynamic and Variable Storage
The cv：：Mat Class： N-Dimensional Dense Arrays
Creating an Array
Accessing Array Elements Individually
The N-ary Array Iterator： NAryMatIterator
Accessing Array Elements by Block
Matrix Expressions： Algebra and cv：：Mat
Saturation Casting
More Things an Array Can Do
The cv：：SparseMat Class： Sparse Arrays
Accessing Sparse Array Elements
Functions Unique to Sparse Arrays
Template Structures for Large Array Types
Summary
Exercises
5.Array Operati0ns
More Things You Can Do with Arrays
cv：：abs0
cv：：absdiff（）
cv：：add0
cv：：addWeighted（）
cv：：bitwise_and（）
……
6.Drawing and Annotating
7.Functors in OpenCV
8.Image， Video， and Data Files
9.Cross-Platform and Native Windows
10.Filters and Convolution
11.General Image Transforms
12.Image Analysis
13.Histograms and Templates
14.Contours
15.Background Subtraction
16.Keypoints and Descriptors
17.Tracking
18.Camera Models and Calibration
19.Projection and Three-Dimensional Vision
20.The Basics of Machine Learning in OpenCV
21.StatModel： The Standard Model for Learning in OpenCV
22.Object Detection
23.Future of OpenCV
A.Planar Subdivisions
B.opencv_contrib
C.Calibration Patterns
Bibliography
Index
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