Computational PhotographyΒΆ
- Introduction
- Basics for Image Processing
- Digital Image
- Cameras
- Fourier Transform
- Image Transformation
- Video Processing
- Light Field
- Resources
- Questions
- Computational Photography: Epsilon to Coded Photography
- Show the lighting and effect
- Computational illumination
- Digital Image Formats
- Digital Image - Processing and Filtering
- Point-process: Pixel / Point Arithmetic
- Image Processing, Filtering via Convolution and Correlation
- Arithmetic Blend modes
- Advanced Modes
- Summary
- Lesson Objectives
- A mathematical representation for smoothing
- Box Filter (Averaging) for Smoothing
- Special Case: Median Filtering
- Next Class
- Cross-Correlation Method
- Example: Box Filter
- Example: Gaussian Filter
- Using Gaussian Filters for Smoothing
- Convolution Method
- Linear Filters
- Image Analysis and Edge Detection
- Good features to match between images
- Images as Functions F(x, y)
- Edge Detection
- Derivatives of F(x, y) to get Edges
- Edge Detection
- Computing Discrete Gradients
- Various Kernels for Computing Gradients
- Canny Edge Detector
- Notes
- Image Transformations
- 1 - Intro
- 2 - Lesson Objectives
- 3 - Image Transformations
- 4 - Parametric Global Warping
- 5 - Parametric Global Warping Functions
- 6 - Image Scaling 2D
- 7 - 2D Image Transformations
- 8 - 2D Rotation
- 9 - 2D Linear Transformations
- 10 - 2D Translation
- 11 - Homogeneous Coordinates
- 12 - Basic 2D Transformation
- 13 - Basic 2D Transformation p2
- 14 - Affine Transformations
- 15 - Projective Transformations
- 16 - Recovering Transformations
- 17 - Translation
- 18 - Translation Solution
- 19 - Rotation
- 20 - Rotation Solution
- 21 - Affine
- 22 - Affine Solution
- 23 - Projective
- 24 - Projective Solution
- 25 - 2D Image Transformations
- 26 - Translation Demo
- 27 - Rotation Demo
- 28 - Shear Demo
- 29 - Affine Warp Demo
- 30 - Perspective Warp Demo
- 31 - Warping
- 32 - Summary