This course provides an in-depth introduction to computer vision.
The goal of computer vision is to compute properties of our world such as the 3D shape of an environment, the motion of objects or the names of people or things. This is done through analysis of digital images or videos. The course covers a range of topics, including feature detection, motion estimation, panoramas, 3D shape reconstruction, and object detection and recognition. This course emphasizes hands-on experience with computer vision, with several large programming projects.
This course will be self-contained. Student do not need to have computer vision background, however, knowledge of the following is required:
  • Working knowledge of MATLAB and preferably also C/C++
  • Linear algebra
  • Basic probability theory and statistics
  • Recommended: Vector calculus, Data structures
The course will have readings from Computer Vision: Algorithms and Applications (available online), by Richard Szeliski.