Introduction

This course serves as an introduction to Computational Photography, a study of the perceptual and technical aspects of the capture and depiction of reality by computational means.

Topics include basic image operations, filters, transformations and feature point detection. Applications such as creating panorams, high-dynamic range images, image morphing, inpainting and retargeting will be used to illustrate applicability of the basic operations. There are programming assignments in this class, and a familarity with Python is required. A working knowledge of linear algebra is helpful, and we will brush up against topics from calculus and probability as well.

Weekly Schedule & Resources

Week 1: Welcome, OpenCV, Digital Images

• Course Textbook - Computer Vision: Algorithms and Applications, 2nd ed. Richard Szeliski.


• Textbook Readings:
  • The Bidirectional Reflectance Distribution Function (BRDF) section on pages 68-69 - Optionally you may want to skim the diffuse & specular sections that follow.
  • Section 2.3 The digital Camera (on pages 79-84)
  • Section 10.0 (pg 609) - Introduction to the Computational Photography Chapter


• Installing Python (3.10) and OpenCV (4.5.4):
  • We recommend Anaconda
  • Main OpenCV Website
  • SciPy.org
  • On Ubuntu: sudo apt-get install python3-opencv python3-scipy python3-numpy
  
  
  • OpenCV Code Examples
    • blueSky.jpg
    • holes.jpg
    • slice-Dice.py - Python slicing operators with images.
    • a.jpg
    • b.jpg
    • blending.py - Full image point operations using numpy array math.
  
  
  • OpenCV Documentation & Tutorials
    
    • OpenCV-Python Tutorial
    • API Documentation (For both C & Python, not all functions will have Python documentation...)
  
  
  • Turning a Paris Apartement into a Giant Camera!
  • Dual Photography [YouTube Video]

  Assignments

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  Student Portfolio

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