|
CS40840: Introduction to Computer Vision
Fall 2025
|
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 |
Dongyoung Choi (Head TA, ex. 7864, )
Harin Kim (Sub-head TA, ex. 7864, )
Hyeongjoon Cho (ex. 7864, )
Jiwoong Na (ex. 7864,
)
Seungmin Hwang (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. |
Tentative schedule |
(Note that this curriculum will be revised adaptively.) |
| |
Index |
Date |
Lecture |
Slides |
HW |
Remarks |
| |
1 |
09/2 |
Introduction to computer vision, Light |
KLMS |
|
|
| |
2 |
09/4 |
No lecture |
KLMS |
|
|
| |
3 |
09/09 |
Human visual system |
KLMS |
hw1 |
|
| |
4 |
09/11 |
Color camera, photography |
KLMS |
|
|
| |
5 |
09/16 |
Digital imaging |
KLMS |
|
|
| |
6 |
09/18 |
Image filter |
KLMS |
|
|
| |
7 |
09/23 |
Fourier series & transform |
KLMS |
hw2 |
|
| |
8 |
09/25 |
Image formation of camera |
KLMS |
|
|
| |
9 |
09/30 |
Epipolar geometry |
KLMS |
|
|
| |
10 |
10/2 |
Homography, calibration, thin-lens optics |
KLMS |
|
|
| |
11 |
10/7 |
Stereo matching (video) |
KLMS |
|
|
| |
12 |
10/9 |
Multiview geometry (video) |
KLMS |
hw3 |
|
| |
13 |
10/14 |
3D scanning workflow |
KLMS |
|
|
| |
|
10/21 |
Mid-term exam |
|
|
|
| |
15 |
10/28 |
Feature detection (Harris corner detector) |
KLMS |
|
|
| |
16 |
11/4 |
Feature matching (blob detection) |
KLMS |
|
|
| |
17 |
11/6 |
Feature descriptor (SIFT) |
KLMS |
|
|
| |
18 |
11/11 |
Optical flow and tracking |
KLMS |
hw4 |
|
| |
19 |
11/13 |
Machine learning for computer vision |
KLMS |
|
|
| |
20 |
11/18 |
Linear regression and denoising |
KLMS |
|
|
| |
21 |
11/20 |
RANSAC, generalization error |
KLMS |
|
|
| |
22 |
11/25 |
Classification |
KLMS |
|
|
| |
23 |
12/04 |
Clustering, dimension reduction |
KLMS |
|
|
| |
24 |
12/09 |
Recognition (Bag-of-words) |
KLMS |
hw5 |
|
| |
25 |
12/11 |
Learning for computer vision |
KLMS |
|
|
| |
|
12/16 |
Final exam |
|
|
|
| |
|
|
|
|
|
|
| |
|
|
|
|
|
|
| |
|
|
|
|
|
|
| |
|
|
|
|
|
|
| |
|
|
|
|
|
|
| |
|
|
|
|
|
|
Grading |
Attendance (10%), mid-term exam (30%), final exam (30%), homework assignments (30%) |
|
|
|
|
Hosted by Visual Computing Laboratory, School of Computing, KAIST.
|
 |
|
