1A-1.Introduction
介绍0
优达学城《计算机视觉概论》
佐治亚理工学院提供 CS6476
介绍1
计算机视觉：
- 理解图像：识别图像中的物体，识别场景
- 理解视频：分类、行为
应用：
- OCR
- 人脸识别
- 物体识别
- 谷歌眼镜
- 3D引擎
- 3D建模
- 自动驾驶
- 体育竞技中的跟踪、（犯规）行为识别等
- 视频游戏（深度相机，实时）
- 医学影像
计算机视觉构成：
- 数学
- 算法
- 图像
Contents
- 1A Introduction
- 2A
- Images as functions
- Filtering
- Linearity and convolution
- Filters as templates
- Edge detection: Gradients
- Edge detection: 2D operators
- 2B
- Hough transform: Lines
- Hough transform: Circles
- Generalized Hough transform
- 2C
- Fourier transform
- Convolution in frequency domain
- Aliasing(锯齿，走样)
- 3A
- Cameras and images
- Perspective imaging
- 3B
- Stereo geometry
- Epipolar geometry
- Stereo correspondence
- 3C
- Extrinsic camera parameters
- Instrinsic camera parameters
- Calibrating cameras
- 3D
- Image to image Projections
- Homographies and mosaics
- Projective geometry
- Essential matrix
- Fundamental matrix
- 4A
- Introduction to "features"
- Finding corners
- Scale invariance
- 4B
- SIFT descriptor
- Matching feature points (a little)
- 4C
- Robust error functions
- RANSAC
- 5A
- Photometry
- 5B
- Lightness
- 5C
- Shape from shading
- 6A
- Introduction to motion
- 6B
- Dense flow: Brightness constraint
- Dense flow: Lucas and Kanade
- Hierarchical LK
- Motion models
- 7A
- Introduction to tracking
- 7B
- Tracking as inference
- The Kalman filter
- 7C
- Bayes filters
- Particle filters
- Particle filters for localization
- Particle filters for real
- 7D
- Tracking considerations
- 8A
- Introduction to recognition
- 8B
- Classification: Generative models
- Principle Component Analysis
- Appearance-based tracking
- 8C
- Discriminative classifiers
- Boosting and face detection
- SVM
- Bag of visual words
- 8D
- Introduction to video analysis
- Activity recognition
- Hidden Markov Models
- 9A
- Color spaces
- Segmentation
- Mean shift segmentation
- Segmentation by graph partitioning
- 9B
- Binary morphology
- 9C
- 3D perception
- 10A
- The retina
- 10B
- Vision in the brain
