學(xué)習(xí)OpenCV

內(nèi)容概要

《學(xué)習(xí)0penCV》將你置身于迅速發(fā)展的計算機視覺領(lǐng)域。本書作者是免費開源0penCV的發(fā)起人，這本書為你介紹了計算機視覺，例證了如何迅速建立使計算機能“看”的應(yīng)用程序，以及如何基于計算機獲取的數(shù)據(jù)作出決策?！　∮嬎銠C視覺幾乎隨處可見：安全系統(tǒng)、管理檢驗系統(tǒng)、醫(yī)學(xué)圖像分析、無人機等。它將Google地圖和Google地球結(jié)合在一起，在LCD屏幕上核對像素，確保襯衫上的每一個針腳都完全縫合。OpenCV提供了一個簡易實用的計算機視覺框架以及一個含有超過500種可以實時運行視覺代碼的函數(shù)的綜合庫?！秾W(xué)習(xí)OpenCV》在每一章里教授任何OpenCV的開發(fā)者或熱愛者如何在這些實戰(zhàn)經(jīng)驗的幫助下迅速掌握該軟件。這本書包括了如下內(nèi)容：　　對0penCV全面詳盡的介紹　　從照相機中導(dǎo)入圖片　　轉(zhuǎn)換圖像　　分割圖像和形狀匹配　　模式識別，包括人臉檢測    　　兩到三個計量單位間的跟蹤和運動　　立體視覺中的3D再現(xiàn)　　機器學(xué)習(xí)算法　　使機器能看見是一個具有挑戰(zhàn)卻又充滿樂趣的目標(biāo)。無論你是想建立一個簡單的還是復(fù)雜的視覺應(yīng)用程序，《學(xué)習(xí)OpenCV》都是你入門的必備教材。

作者簡介

倫比亞大學(xué)計算機科學(xué)教授Gary Rost BradSki博士，斯坦福大學(xué)人工智能實驗室的顧問教授，也是Willow Garage公司機器；人學(xué)研究協(xié)會的資深科學(xué)家。Ad rian Kaehler博士，Applied Mind S公司的資深科學(xué)家，從事機器學(xué)習(xí)、統(tǒng)計建模、計算機視覺和機器人學(xué)方面的研究。

書籍目錄

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

章節(jié)摘錄

　　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.

媒體關(guān)注與評論

　　“這本寶庫對專業(yè)人員來講非常有用，對初涉這個領(lǐng)域的人們也是個絕好的工具。像其宣傳的那樣，它是一組計算機視覺算法?！薄　　猈illiam T．Freeman．　　麻省理工學(xué)院計算機科學(xué)與人工智能實驗室“《學(xué)習(xí)0penCV》即將占據(jù)每從從事計算機視覺領(lǐng)域的人的書架上最顯著的一處。”　　——David Lowe，英屬哥

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