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Computational Photography is an emerging new field created by the convergence of computer graphics, computer vision and photography. Its role is to overcome the limitations of the traditional camera by using computational techniques to produce a richer, more vivid, perhaps more perceptually meaningful representation of our visual world. I will take notes for Computational Photography from Udacity in this series, main content is shown as follow.
Module 1: Introduction
- What is Computational Photography?
- Examples of Computational Photography to provide context.
- Overview of the scope of Computational, with recpect to other disciplines, and its potential impact.
- Assignment: Getting set up and sharing some pictures!
Module 2: Image processing and analysis
- Digital image representation
- Pixel/point processes for images
- Smoothing and Filtering methods for images
- Extracting Features from images
- Assignments: Experiments with Image filtering, features detection
Module 3: Cameras, Optics and Sensors
- Pin-Hole Camera
- Importance of Optics
- How does a camera work?
- Sensors
- Assignments: Epsilon Photography & make your own Camera obscura
Module 4: Image Blending / Merging
- Sampling and Frequencies
- Image Blending
- Image Features
- Homeworks: Exercise on Image Blending
Module 5: Doing Computational Photography
- Panoramas
- HDR
- Image Editing
- Assignments: Experiments with HDR / Panoramas
Module 6: Editing to Video
- Video Textures
- Video Stabilization
- Assignments: Experiments with Video Textures
Module 7: Computational Cameras
- Light filed cameras
- multi-view
- Projector Camera Systems
Module 8: Advanced Topics. Special Cases
- Newer camera technologies
- Blur / Deblur
- Social / crowd Photography
- Final Project
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