学习OpenCV

章节摘录

　　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 Intels 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 OpenCVs goals is to provide a simple-to-use computer vision infrastructure that helps people build fairly sophisticated vision applications 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 OpenCVs 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.

媒体关注与评论

　　“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最显著的一处。”　　——David Lowe，英属哥

内容概要

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。

书籍目录

Preface1. 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  Exercises2. 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  Exercises3. Gettingto KnowOpenCV  OpenCV Primitive Data Types  CvMat Matrix Structure  IplImage Data Structure  Matrix and Image Operators  Drawing Things  Data Persistence  Integrated Performance Primitives  Summary  Exercises4.  HighGUI  A Portable Graphics Toolkit  Creating a Window  Loading an Image  Displaying Images  Working with Video  ConvertImage  Exercises5. Image Processing  Overview  Smoothing  Image Morphology  Flood Fill  Resize  Image Pyramids  Threshold  Exercises6. 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  Exercises7. Histograms and Matching  Basic Histogram Data Structure  Accessing Histograms  Basic Manipulations with Histograms  Some More Complicated Stuff  Exercises8. Contours  Memory Storage  Sequences  Contour Finding  Another Contour Example  More to Do with Contours  Matching Contours  Exercises9. Image Parts and Segmentation  Parts and Segments  Background Subtraction  Watershed Algorithm  Image Repair by Inpainting  Mean-Shift Segmentation  Delaunay Triangulation, Voronoi Tesselation  Exercises10. 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  Exercises11. Camera Models and Calibration  Camera Model  Calibration  Undistortion  Putting Calibration All Together  Rodrigues Transform  Exercises12. 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  Exercises13. 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　Exercises14. OpenCV's Future  Past and Future　Directions　OpenCV for Artists　Afterword　BibliographyIndex

作者简介

《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。

图书封面

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