Learning OpenCV的中文版

O'REILLY最近刚刚出版了一本相当不错的OpenCV书籍，名字叫《Leaning OpenCV》，该书的作者是很出名的 Gary Bradski 和 Adrain Kaebler。Gary目前在斯坦福大学的人工智能实验室，是OpenCV的发起人。此书具有这样的“渊源”，自然不凡了。

欣闻此书的出版，获准将其翻译为中文，并从清华大学出版社得到样书，也算是幸事一件！

该书的特点大致是对OpenCV的很多基本算法函数都给出了详细的阐述，算法说明非常到位。在阅读的过程中，不但有“知其然”而且也有“知其所以然”的感受。

正在努力工作，希望尽快把中文稿提供给国内的读者！

先给出本书的目录，供大家欣赏！

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Preface
Chapter 1. Overview
Section 1.1. What Is OpenCV?
Section 1.2. Who Uses OpenCV?
Section 1.3. What Is Computer Vision?
Section 1.4. The Origin of OpenCV
Section 1.5. Downloading and Installing OpenCV
Section 1.6. Getting the Latest OpenCV via CVS
Section 1.7. More OpenCV Documentation
Section 1.8. OpenCV Structure and Content
Section 1.9. Portability
Section 1.10. Exercises
Chapter 2. Introduction to OpenCV
Section 2.1. Getting Started
Section 2.2. First Program—Display a Picture
Section 2.3. Second Program—AVI Video
Section 2.4. Moving Around
Section 2.5. A Simple Transformation
Section 2.6. A Not-So-Simple Transformation
Section 2.7. Input from a Camera
Section 2.8. Writing to an AVI File
Section 2.9. Onward
Section 2.10. Exercises
Chapter 3. Getting to Know OpenCV
Section 3.1. OpenCV Primitive Data Types
Section 3.2. CvMat Matrix Structure
Section 3.3. IplImage Data Structure
Section 3.4. Matrix and Image Operators
Section 3.5. Drawing Things
Section 3.6. Data Persistence
Section 3.7. Integrated Performance Primitives
Section 3.8. Summary
Section 3.9. Exercises
Chapter 4. HighGUI
Section 4.1. A Portable Graphics Toolkit
Section 4.2. Creating a Window
Section 4.3. Loading an Image
Section 4.4. Displaying Images
Section 4.5. Working with Video
Section 4.6. ConvertImage
Section 4.7. Exercises
Chapter 5. Image Processing
Section 5.1. Overview
Section 5.2. Smoothing
Section 5.3. Image Morphology
Section 5.4. Flood Fill
Section 5.5. Resize
Section 5.6. Image Pyramids
Section 5.7. Threshold
Section 5.8. Exercises
Chapter 6. Image Transforms
Section 6.1. Overview
Section 6.2. Convolution
Section 6.3. Gradients and Sobel Derivatives
Section 6.4. Laplace
Section 6.5. Canny
Section 6.6. Hough Transforms
Section 6.7. Remap
Section 6.8. Stretch, Shrink, Warp, and Rotate
Section 6.9. CartToPolar and PolarToCart
Section 6.10. LogPolar
Section 6.11. Discrete Fourier Transform (DFT)
Section 6.12. Discrete Cosine Transform (DCT)
Section 6.13. Integral Images
Section 6.14. Distance Transform
Section 6.15. Histogram Equalization
Section 6.16. Exercises
Chapter 7. Histograms and Matching
Section 7.1. Basic Histogram Data Structure
Section 7.2. Accessing Histograms
Section 7.3. Basic Manipulations with Histograms
Section 7.4. Some More Complicated Stuff
Section 7.5. Exercises
Chapter 8. Contours
Section 8.1. Memory Storage
Section 8.2. Sequences
Section 8.3. Contour Finding
Section 8.4. Another Contour Example
Section 8.5. More to Do with Contours
Section 8.6. Matching Contours
Section 8.7. Exercises
Chapter 9. Image Parts and Segmentation
Section 9.1. Parts and Segments
Section 9.2. Background Subtraction
Section 9.3. Watershed Algorithm
Section 9.4. Image Repair by Inpainting
Section 9.5. Mean-Shift Segmentation
Section 9.6. Delaunay Triangulation, Voronoi Tesselation
Section 9.7. Exercises
Chapter 10. Tracking and Motion
Section 10.1. The Basics of Tracking
Section 10.2. Corner Finding
Section 10.3. Subpixel Corners
Section 10.4. Invariant Features
Section 10.5. Optical Flow
Section 10.6. Mean-Shift and Camshift Tracking
Section 10.7. Motion Templates
Section 10.8. Estimators
Section 10.9. The Condensation Algorithm
Section 10.10. Exercises
Chapter 11. Camera Models and Calibration
Section 11.1. Camera Model
Section 11.2. Calibration
Section 11.3. Undistortion
Section 11.4. Putting Calibration All Together
Section 11.5. Rodrigues Transform
Section 11.6. Exercises
Chapter 12. Projection and 3D Vision
Section 12.1. Projections
Section 12.2. Affine and Perspective Transformations
Section 12.3. POSIT: 3D Pose Estimation
Section 12.4. Stereo Imaging
Section 12.5. Structure from Motion
Section 12.6. Fitting Lines in Two and Three Dimensions
Section 12.7. Exercises
Chapter 13. Machine Learning
Section 13.1. What Is Machine Learning
Section 13.2. Common Routines in the ML Library
Section 13.3. Mahalanobis Distance
Section 13.4. K-Means
Section 13.5. Na?ve/Normal Bayes Classifier
Section 13.6. Binary Decision Trees
Section 13.7. Boosting
Section 13.8. Random Trees
Section 13.9. Face Detection or Haar Classifier
Section 13.10. Other Machine Learning Algorithms
Section 13.11. Exercises
Chapter 14. OpenCV's Future
Section 14.1. Past and Future
Section 14.2. Directions
Section 14.3. OpenCV for Artists
Section 14.4. Afterword
Chapter 15. Bibliography