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CS484: Introduction to Computer Vision

Fall 2018

 

Instructor

Min H. Kim, [Room] 3429, [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

Monday and Wednesday 1:00PM—2:30PM, Rm. 1501, Bldg. E3-1

TA office hours

Monday and Wednesday 6:00PM—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

Reinhard Klette (2014) Concise Computer Vision, Springer [site]
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 revised adaptively.)

  Week Date Lecture Readings Slides HW Due
  1 08/27 Introduction to computer vision Klette: 1, Szeliski: 2.2, 2.3 slide01    
    08/29 Light and color Klette: 1, Szeliski: 2.2, 2.3 slide02    
  2 09/03 Color vector and transformation Klette: 1, Szeliski: 2.2, 2.3 slide03    
    09/05 Color space and gamma correction Klette: 1, Szeliski: 2.2, 2.3 slide04    
  3 09/12 Convolution and cross-correlation Klette: 1, 2, Szeliski: 3.2 slide05 HW1 9/16,23:55
  4 09/17 Spatial vs. frequency domain Klette: 1, 2, Szeliski: 3.4 slide06 HW2 9/30, 23:55
    09/19 Fourier series and Fourier transforms Klette: 1, 2, Szeliski: 3.5.2, 8.1.1 slide07    
  5 09/24 Mid-Authumn Festival Days      
  6 10/01 Fourier transforms and convolution Theorem Klette: 1, 2, Szeliski: 3.5.2, 8.1.1      
    10/03 Edge detectors and image pyramids   HW3 TBA
  7 10/8 Camera: geometric optics of a digital camera, image formation model      
    10/10 Image analysis: line and circle detection and 2D shape analysis   HW4 TBA
  8 10/15 Mid-term exam (TBA)        
  9 10/22 Motion analysis: 3D motion and 2D optical flow      
    10/24 Image segmentation: image and video segmentation      
  10 10/29 Computational photography: light-field imaging      
    10/31 Computational photography: HDR imaging   HW5 TBA
  11 11/05 3D shape reconstruction: surface topology and structured lighting      
    11/07 3D shape reconstruction: photometric stereo, Epipolar geometry, RANSAC and SfM   HW6 TBA
  12 11/12 Stereo matching: stereo vision, matching cost, dynamic programming matching      
    11/14 No lecture (Microsoft Faculty Summit)        
  13 11/19 Object detection: Haar Wavelets and random decision forests      
    11/21 Recognition overview and bag of features  
  14 11/26 Large-scale instance recognition        
    11/28 Machine learning: supervised learning vs. unsupervised learining      
  15 12/03 Convolutional neural networks (CNN)      
    12/05 No lecture (SIGGRAPH Asia 2018)      
  16 12/10 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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