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KTP592: Computer Vision and Machine Learning

Spring 2023


Min Hyuk Kim, [email]
Tae-kyun Kim, [email]
Seunghoon Hong, [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

1—5 weeks, Prof. Min H. Kim, Wednesday 14:00—17:00, Lecture rooms: KAIST Dogok campus or online
6—10 weeks, Prof. Tae-Kyun Kim, Wednesday 14:00—17:00, Lecture rooms: KAIST Dogok campus or online
11—15 weeks, Prof. Seunghoon Hong, Thursday 19:00—22:00, Lecture rooms: KAIST Dogok campus or online

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]


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 Professor Slides  
  1 3/1 No lecture (national holiday) Min H. Kim KLMS  
  2 3/8 Introduction, color camera and transform Min H. Kim KLMS  
  3 3/15 Image filter, frequency domain, point and corner Min H. Kim KLMS  
  4 3/22 Feature matching, feature descriptor, bag-of-words Min H. Kim KLMS  
  5 3/29 Image formation model and thin lens optics Min H. Kim KLMS  
  6 4/5 Machine learning for computer vision Tae-Kyun Kim KLMS  
  7 4/12 Deep learning introduction Tae-Kyun Kim KLMS  
    4/17--21 Mid-term exam period      
  8 4/19 Topics in deep learning and vision Tae-Kyun Kim KLMS  
  9 4/26 Generative networks Tae-Kyun Kim KLMS  
  10 5/4 Data augmentation Tae-Kyun Kim KLMS  
  11 5/13 Semantic segmentation Seunghoon Hong KLMS  
  12 5/25 Object detection Seunghoon Hong KLMS  
  13 6/1 Motion and tracking Seunghoon Hong KLMS  
  14 6/8 Video recognition Seunghoon Hong KLMS  
  15 6/15 Attention and Transformers for vision Seunghoon Hong KLMS  
    6/12--16 Final-term exam period      


First quiz (30%), second quiz (30%), third quiz (30%), attendance (10%)

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