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CS576: Computer Vision

Spring 2019

 

Instructor

Min H. Kim, [Room] 3429, [email]

Course description

 

This course provides advanced topics to computer vision, including the foundations and applications 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 advanced computer vision applications.

Lecture time and place

Monday and Wednesday 1:00PM—2:30PM, Rm. 117, Bldg. N-1

TA office hours

(Suk Jun Jeon, Hyeonjoong Jang) Tuesday and Thursday 2:30PM—5:30PM, Rm. 2443, Bldg. E3-1
(Inseung Hwang) Thursday 2:30PM—5:30PM, Rm. 2443, Bldg. E3-1

Teaching assistants

Suk Jun Jeon, [e-mail]
Hyunho Ha, [e-mail]
Hyeonjoong Jang, [e-mail]
Shinyoung Yi, [e-mail]
Inseung Hwang, [e-mail]

Textbook

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

References

Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer [site]
Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016) Deep Learning, MIT Press [site]
Gary Bradski, Adrian Kaehler (2016) Learning OpenCV 3, O'Reilly Media [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

 

  Index Date Lecture Readings Slides HW Due
  1 2/25 Introduction to computer vision chapter 1 slide01   
  2 2/27 Image formation and camera chapter 2 slide02   
  3 3/4 Epipolar geometry chapter 11 slide03    
  4 3/6 Camera calibration chapters 6 and 11 slide04   
  5 3/11 Stereo matching chapters 6 and 11 slide05v2    
  6 3/13 Structure from motion chapters 7 slide06  
  7 3/18 Thin lens and optics chapters 2 and 10 slide07    
  8 3/20 Light-field imaging chapter 13 slide08v2 HW1v3 4/21 23:59
  9 3/25 High dynamic range (HDR) imaging chapter 10 slide09    
  10 3/27 High dynamic range (HDR) rendering chapter 10 slide10    
  11 4/1 Light chapter 2 slide11    
  12 4/3 Color chapter 2 slide12    
  13 4/8Frequency domain chapters 3 and 8 slide13    
  14 4/10Hyperspectral imaging chapters 2, 4 and 10 slide14    
    4/15 Mid-term exam        
  15 4/22 Machine learning for computer vision chapters 3, 4 and 5 slide15    
  16 4/24 Dimension reduction chapters 3, 4 and 5 slide16  
  17 4/29 Linear regression chapters 3, 4 and 5 slide17   
  18 5/1 Classification (1) chapters 3, 4 and 5 slide18    
  19 5/8 Classification (2) chapters 3, 4 and 5 slide18    
  20 5/13 Clustering chapters 5 slide19   
  21 5/15 Recognition (1) chapter 14 slide20    
  22 5/27 Deep learning & neural networks chapter 14 slide21 HW2 6/16 23:59
  23 5/29 Convolutional neural networks chapter 14 slide22v4
  24 6/3 Training networks chapter 14 slide23v3  
  25 6/5 Understanding what networks learn chapter 14 sdlie24    
    6/10 Final exam        
               

Grading

Attendance (10%), midterm/final exams (50%), assignments (30%), quizzes (10%)

Resources

Computer Graphics papers
Computer Vision papers

http://kesen.realtimerendering.com/
http://www.cvpapers.com/

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

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