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CS484: Introduction to Computer Vision

Fall 2021

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 [refer to KLMS]

TA office hours

Wednesday and Friday 1:00PM—4:00PM, Zoom meeting [refer to KLMS]
(contact TAs for appoinments by email in advance)

Teaching Assistants

Dahyun Kang (ex. 7864, )
Inseung Hwang (ex. 7864, )
Donggun Kim (ex. 7864, )
Jungwoo Kim (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]
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 08/31 Introduction to computer vision slide01    
  2 09/02 Light slide02    
  3 09/07 Light, color camera, photography slide03v2 hw1 9/15, 23:55 
  4 09/09 Color imaging slide04    
  5 09/14 Image filter slide05    
  6 09/16 Fourier Series & Transforms slide06 hw2v2 10/3, 23:55
    09/21,23 No lecture (Chuseok Holidays)      
  7 09/28 Image formation of camera slide07v5    
  8 09/30 Epipolar geometry slide08    
  9 10/05 Homography, camera calibration slide09v2    
  10 10/07 Stereo matching slide10v5 hw3 10/29, 23:55
  11 10/12 Multiview geometry slide11    
  12 10/14 3D scanning workflow slide12v3    
    10/19 Mid-term exam (Tuesday, 13:00 ~ 15:45)      
  13 10/26 Feature detection (Harris corner detector) slide13v2    
  14 11/02 Feature matching (blob dectection) slide14    
  15 11/04 Feature descriptor (SIFT) slide15 hw4 11/19, 23:55
  16 11/09 Optical Flow & Tracking slide16    
  17 11/11 Machine learning for computer vision slide17    
  18 11/16 Linear regression and denosing slide18    
  19 11/18 GLM, RANSAC, generalization error slide19    
  20 11/23 Classification slide20    
  21 11/25 Clustering slide21    
  22 11/30 Recognition (Bag-of-words) slide22 hw5 12/16, 23:55
  23 12/07 Dimension reduction & introduction to network slide23(1), slide23(2)    
  24 12/09 Intro and learning convolutional neural network slide24(1)v2, slide24(2)  
    12/14 Final exam (Tuesday, 13:00 ~ 15:45)      
             
             
             
             

Grading

Attendance (10%), mid-term exam (25%), final exam (25%), homework assignments (40%)

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

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