CS30750 (a.k.a. CS484): Introduction to Computer Vision

Fall 2026

Instructor

Min Hyuk Kim, [Room] 2403, [email]

Course description

This course provides a comprehensive introduction to low-level computer vision, including the foundations of camera image formation, geometric optics, feature detection, stereo matching, motion estimation, image recognition, scene understanding, etc. This course will help students develop intuitions and mathematics of various computer vision applications.

Lecture time and place

Tuesday and Thursday 1:00PM—2:30PM, E3-1, Rm. 1501

TA office hours

Tuesday and Thursday 3:00PM—6:00PM, E3-1, Rm. 2401

Teaching Assistants

Hyeongjun Cho (ex. 7864, )
Harin Kim (ex. 7864, )
Seung Chan Hwang (ex. 7864, )
Seungmin Hwang (ex. 7864, )
Jongmo Park (ex. 7864, )

Reference books

Richard Szeliski (2010) Computer Vision: Algorithms and Applications, Springer [site]
Richard Hartley and Andrew Zisserman (2011) Multiple View Geometry in Computer Vision, Cambridge Press [site]
Xiang Gao, Tao Zhang (2011) Introduction to Visual SLAM: From Theory to Practice, Splinger [site]
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer [site]
Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016) Deep Learning, MIT Press [site]

Prerequisites

There are no official course prerequisites. Basic knowledge of Python and LaTeX is fundamentally required to fulfill homework tasks.

Course goal

Student will establish theoretical and practical foundations of computer vision and be familiar with various computer vision applications.

Grading

Attendance (10%), mid-term exam (35%), final exam (35%), homework assignments (20%)

Tentative schedule

(Note that this curriculum will be revised adaptively.)