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CS484: Introduction to Computer Vision

Fall 2020

Instructor

Min H. 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, Zoom meeting

TA office hours

Tuesday and Thursday 2:30PM—5:30PM, Zoom meeting
(contact TAs for appoinments by email in advance)

Teaching Assistants

Hyeonjoong Jang (ex. 7864, )
Inseung Hwang (ex. 7864, )

Reference books

Richard Szeliski (2010) Computer Vision: Algorithms and Applications, Springer [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.

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 Due
  1 09/01 Introduction to computer vision slide01    
  2 09/03 Light slide02    
  3 09/08 Color imaging (1) slide03    
  4 09/10 Color imaging (2) slide03    
  5 09/15 Image filter slide04    
  6 09/17 Frequency domain slide05 HW1 9/20, 23:59
  7 09/22 Image formation of camera slide06 HW2 10/4 23:59
  8 09/24 Epipolar geometry slide07    
  9 09/29 Camera calibration slide08    
    10/01 No lecture (Chuseok Holidays)      
  10 10/06 Stereo matching slide09(v.2)    
  11 10/08 Multiview geometry slide10    
  12 10/13 3D scanning workflow slide11    
  13 10/15 Thin lens optics slide12 HW3 11/01 23:59
  14 10/19 Mid-term exam period      
  15 10/27 Feature detection (Harris corner detector) slide13    
  16 10/29 Feature matching (blob dectection) slide14    
  17 11/03 Feature descriptor (SIFT) slide15 HW4 11/15 23:59
  18 11/05 Machine learning for computer vision slide16    
  19 11/10 Linear regression slide17v2    
  20 11/12 GLM, RANSAC, generalization error slide18    
  21 11/19 Classification slide19v2    
  22 11/24 Clustering slide20    
  23 11/26 Dimension reduction slide21    
  24 12/01 Recognition slide22 HW5 12/20 23:59
  25 12/03 Introduction to CNN slide23v2  
  26 12/08 Training CNN slide24    
  27 12/10 No lecture (KAIST enterance exam)      
  28 12/15 Final exam (Tuesday, 13:00 ~ 15:45)      
             
             

Grading

Attendance (10%), final exam (50%), assignments (40%)

Hosted by Visual Computing Laboratory, School of Computing, KAIST.

KAIST