You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

  • Lecture 1- Introduction
    tl;dr: Overview of the course, objectives, and introduction to computer vision.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski (2nd Edition) Chapter 1
    2. Szeliski 3.1 - 3.3
  • Lecture 2 - Image Formation and Camera Models
    tl;dr: Understanding how images are formed and the basic principles of camera models.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski 3.1-3.3, 7.2
    2. Szeliski 2.3.1, 3.4-3.5
  • Lecture 3 - Features Detection and Invariance.
    tl;dr: Understanding how to detect features in images and make them invariant to transformations.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski 7.1
    2. Szeliski 7.1
  • Lecture 4 - Image Transformations & Image Alignments.
    tl;dr: Understanding how to transform images and align them.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski 3.6
    2. Szeliski 6.1
  • Lecture 5 - RANSAC & Camera Calibration.
    tl;dr: Understanding how to estimate the camera parameters and how to robustly estimate the parameters using RANSAC.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski 6.1
    2. Szeliski 2.1.3-2.1.6
  • Lecture 6 - Panoramas & Single View Modling.
    tl;dr: Understanding how to create panoramas and model a scene from a single image.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski 8
    2. Szeliski 11.1
  • Lecture 7 - Stereo & Light and perception.
    tl;dr: Understanding how to estimate depth from stereo images and how light and perception are related.
    [notes] [Jupyter Notebook] [slides1] [slides2] [all]

    Readings:

    1. Szeliski 12.3-12.5
    2. Szeliski 2.2