CS4670/5670 - Computer Vision

Picture credit: xkcd

About this course

Humans are extremely good at perceiving the world from visual input alone. This comes so easily to us that we underestimate how difficult perception it is, and how hard it is for machines, as the webcomic above illustrates.
Computer vision is a subfield of AI focussed on getting machines to see as humans do, and has been around for almost half a century. This course will cover the basics of computer vision: the underlying mechanics of images, the core problems that the field focuses on, and the array of tools and techniques that have been developed. The emphasis will be on covering the fundamentals which underly both computer vision research and applications. A tentative list of topics is below:
  • Geometry / Physics of image formation
  • Properties of images and basic image processing
  • 3D reconstruction
  • Grouping (of image pixels into objects)
  • Machine learning in computer vision: basics, hand-designed feature vectors, convolutional networks
  • Detecting and localizing objects
A detailed but tentative list of learning outcomes can be found below. This course is intended for undergraduate students and MEng. students. Knowledge of basic probability and linear algebra will be useful. A primer on the aspects of linear algebra that will be useful is available here.

Quick info

Course Modality: Online, synchronous (for those in convenient time zones) and asynchronous (otherwise)
Ed discussions (in lieu of Piazza)
Instructor: Bharath Hariharan
Lecture time: MWF 1:30pm - 2:20 pm
Lecture zoom
  • Aaron Gokolsan
  • Albert Tsao
  • Ankush Rayabhari
  • Ashneel Das
  • Clara Song
  • Dongqing Wang
  • Jason Pan
  • Joshua Loomasney
  • Julie Phan
  • Kabir Walia
  • Shreeya Gad
  • Yiheng Dong
Instructor Office Hours: (TBD)
All Office Hours:(TBD)

Learning outcomes Resources Grading policy