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！

====
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

Learning OpenCV的中文版相关推荐

写在前面的一些话：《Learning OpenCV》中文版 .
2009-09-17 15:51 7578人阅读评论(4) 收藏举报 <!-- /* Font Definitions */ @font-face {font-family:Helvetic ...
basler相机参数简要中文说明_附下载| OpenCV最新中文版官方教程
OpenCV是计算机视觉中经典的专用库,然而其中文版官方教程久久不来.近日,一款最新OpenCV4.1 版本的完整中文版官方教程出炉,读者朋友可以更好的学习了解OpenCV相关细节.教程根据官方提供的 ...
撒花！吴恩达《Machine Learning Yearning》中文版新鲜出炉！
红色石头的个人网站:www.redstonewill.com 我相信红色石头的很多读者朋友都受惠于吴恩达的 deeplearning.ai 专项课程.红色石头也不例外,之前在我的公众号里连载了 dee ...
【福利】OpenCV最新中文版官方教程来了（附下载）
OpenCV 中文版官方教程来了. OpenCV是计算机视觉中经典的专用库,然而其中文版官方教程久久不来.近日,一款最新OpenCV4.1 版本的完整中文版官方教程出炉,读者朋友可以更好的学习了解Op ...
吴恩达《Machine Learning Yearning》中文版！
吴恩达老师在离开百度之后创建了deplearning.ai,在AI研究的道路上从未停下脚步,在其网站上不仅更新了AI课程,还有最新力作<Machine Learning Yearning> ...
deep learning with python 中文版-Deep Learning with Python
一.导论 1.1 人工智能.机器学习.深度学习人工智能.机器学习人工智能:1980年代达到高峰的是专家系统,符号AI是之前的,但不能解决模糊.复杂的问题. 机器学习是把数据.答案做输入,规则作输出 ...
Learning OpenCV Lecture 5 (Filtering the Images)
In this chapter, we will cover: Filtering images using low-pass filters Filtering images using a med ...
Python深度学习(Deep Learning with Python) 中文版+英文版+源代码
Keras作者.谷歌大脑François Chollet最新撰写的深度学习Python教程实战书籍(2017年12月出版) 介绍深入学习使用Python语言和强大Keras库,详实新颖.PDF高清中文 ...
[阅读体会] 学习OpenCV 3 （Learning OpenCV 3）
文章目录书中实用的资料站点第一章书中实用的资料站点示例代码:https://github.com/oreillymedia/Learning-OpenCV-3_examples 书籍官网:ht ...

Learning OpenCV的中文版

Learning OpenCV的中文版相关推荐

最新文章

热门文章