First Principles of Computer Vision
This lecture series on computer vision is presented by Shree Nayar , T. C. Chang Professor of Computer Science at Columbia Engineering. It has been designed for students, practitioners and enthusiasts who have no prior knowledge of computer vision.
- What is Computer Vision?
- What is Vision Used For?
- How Do Humans Do it?
- Topics Covered
- About the Lecture Series
- References and Credits
- Pinhole & Perspective Projection
- Image Formation using Lenses
- Depth of Field
- Lens Related Issues
- Wide Angle Cameras
- Animal Eyes
- A Brief History of Imaging
- Types of Image Sensors
- Resolution, Noise, Dynamic Range
- Sensing Color
- Camera Response & HDR Imaging
- Nature’s Image Sensors
- Geometric Properties
- Segmenting Binary Images
- Iterative Modification
- Pixel Processing
- LSIS and Convolution
- Linear Image Filters
- Non-Linear Image Filters
- Template Matching
- Fourier Transform
- Convolution Theorem
- Filtering in Frequency Domain
- Deconvolution
- Sampling Theory and Aliasing
- What is an Edge?
- Edge Detection Using Gradients
- Edge Detection Using Laplacian
- Canny Edge Detector
- Corner Detection
- Fitting Lines and Curves
- Active Contours
- Hough Transform
- Generalized Hough Transform
- What is an Interest Point?
- Detecting Blobs
- SIFT Detector
- SIFT Descriptor
- 2x2 Image Transformations
- 3x3 Image Transformations
- Computing Homography
- Dealing with Outliers: RANSAC
- Warping and Blending Images
- Uses of Face Detection
- Haar Features for Face Detection
- Integral Image
- Nearest Neighbor Classifier
- Support Vector Machine
- Radiometric Concepts
- Scn. Radiance & Img. Irradiance
- Reflectance Models
- Reflection from Rough Surfaces
- Dichromatic Model
- Gradient Space & Reflectance Map
- Photometric Stereo
- Lambertian Case
- Calibration Based Photo. Stereo
- Shape from Normals
- Interreflections
- Human Perception of Shading
- Stereographic Projection
- Shape from Shading Algorithm
- Shading Illusions
- Point Spread Function
- Depth from Focus
- Depth from Defocus
- Photometric Stereo Systems
- Structured Light Range Finding
- Phase Shifting Method
- Structured Light Systems
- Time of Flight Method
- Linear Camera Model
- Camera Calibration
- Intrinsic and Extrinsic Matrices
- Simple Stereo
- Problem of Uncalibrated Stereo
- Epipolar Geometry
- Estimating Fundamental Matrix
- Finding Correspondences
- Computing Depth
- Stereo Vision in Nature
- Motion Field & Optical Flow
- Optical Flow Constraint Equation
- Lucas-Kanade Method
- Coarse-to-Fine Flow Estimation
- Application of Optical Flow
- Structure from Motion Problem
- Observation Matrix
- Rank of Observation Matrix
- Tomasi-Kanade Factorization
- Change Detection
- Gaussian Mixture Model
- Object Tracking using Template Matching
- Tracking by Feature Detection
- Segmentation by humans
- Segmentation as Clustering
- k-Means Segmentation
- Mean-Shift Segmentation
- Graph Based Segmentation
- Shape vs. Appearance
- Learning Appearance
- Principal Component Analysis
- Finding Principal Components
- PCA and SVD
- Parametric Appearance Representation
- Appearance Matching
- Perceptron Network
- Activation Function
- Neural Network
- Gradient Descent
- Backpropagation Algorithm
- Example Applications
- When to Use Machine Learning?
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This lecture series on computer vision is presented by Shree Nayar, T. C. Chang Professor of Computer Science at Columbia Engineering.It has been designed for students, practitioners and enthusiasts who have no prior knowledge of computer vision.