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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, TBA

TA office hours

Monday and Wednesday 6:30PM—8:00PM, Rm. 402, Bldg. N-1
Thursday 2:30PM—5:30PM, Rm. 404, Bldg. N-1

Teaching assistants

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

Reference books

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

  Week Date Lecture Readings Slides HW Due
  1   Introduction to computer vision        
      Light and color        
  2   Color vector and transformation        
      Color space and gamma correction        
  3   Convolution and cross-correlation     HW1  
  4   Spatial vs. frequency domain     HW2  
      Fourier series and Fourier transforms        
  5   Mid-Authumn Festival Days      
  6   Fourier transforms and convolution Theorem        
      Fourier transforms and convolution Theorem      
  7   Edge detection        
      Corner detection        
  8   Mid-term exam (TBA)        
  9   Invariant feature detection        
      Local image descriptors and matching     HW3  
  10   Segmentation and grouping        
      Fitting, RANSAC, and Hough transform        
  11   Machine learning for computer vision        
  12   Classification (kNN, Bayes, logistic regression, SVM)        
      Recognition     HW4  
  13   Image formation model, perspective projection        
      Thin lense optics and digital camera        
  14   Epipolar geometry for 3D imaging   
  15   3D imaging via stereo matching     HW5  
      3D imaging via stereo matching      
  16   Final exam (TBA)        
               

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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