哥伦比亚大学课程——计算机视觉基本原理
资料链接
- 官方链接 https://fpcv.cs.columbia.edu/
- 课程视频 https://www.bilibili.com/video/BV18w411w7NG?spm_id_from=333.788.videopod.episodes&vd_source=b543022652445d3b2433332fe784ea26
- 课件： https://fpcv.cs.columbia.edu/Monographs
课程内容
- Overview
- What is Computer Vision?
- What is Vision Used For?
- How Do Humans Do it?
- Topics Covered
- About the Lecture Series
- References and Credits
- Overview
- Pinhole & Perspective Projection
- Image Formation using Lenses
- Depth of Field
- Lens Related Issues
- Wide Angle Cameras
- Animal Eyes
- Overview
- A Brief History of Imaging
- Types of Image Sensors
- Resolution, Noise, Dynamic Range
- Sensing Color
- Camera Response & HDR Imaging
- Nature’s Image Sensors
- Overview
- Pixel Processing
- LSIS and Convolution
- Linear Image Filters
- Non-Linear Image Filters
- Template Matching
- Overview
- Fourier Transform
- Convolution Theorem
- Filtering in Frequency Domain
- Deconvolution
- Sampling Theory and Aliasing
- Overview
- What is an Edge?
- Edge Detection Using Gradients
- Edge Detection Using Laplacian
- Canny Edge Detector
- Corner Detection
- Overview
- 2x2 Image Transformations
- 3x3 Image Transformations
- Computing Homography
- Dealing with Outliers: RANSAC
- Warping and Blending Images
- Overview
- Uses of Face Detection
- Haar Features for Face Detection
- Integral Image
- Nearest Neighbor Classifier
- Support Vector Machine
- Overview
- Radiometric Concepts
- Scn. Radiance & Img. Irradiance
- BRDF
- Reflectance Models
- Reflection from Rough Surfaces
- Dichromatic Model
- Overview
- Gradient Space & Reflectance Map
- Photometric Stereo
- Lambertian Case
- Calibration Based Photo. Stereo
- Shape from Normals
- Interreflections
- Overview
- Human Perception of Shading
- Stereographic Projection
- Shape from Shading Algorithm
- Shading Illusions
- Overview
- Photometric Stereo Systems
- Structured Light Range Finding
- Phase Shifting Method
- Structured Light Systems
- Time of Flight Method
- Overview
- Problem of Uncalibrated Stereo
- Epipolar Geometry
- Estimating Fundamental Matrix
- Finding Correspondences
- Computing Depth
- Stereo Vision in Nature
- Overview
- Motion Field & Optical Flow
- Optical Flow Constraint Equation
- Lucas-Kanade Method
- Coarse-to-Fine Flow Estimation
- Application of Optical Flow
- Overview
- Structure from Motion Problem
- Observation Matrix
- Rank of Observation Matrix
- Tomasi-Kanade Factorization
- Overview
- Change Detection
- Gaussian Mixture Model
- Object Tracking using Template Matching
- Tracking by Feature Detection
- Overview
- Segmentation by humans
- Segmentation as Clustering
- k-Means Segmentation
- Mean-Shift Segmentation
- Graph Based Segmentation
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