M1522.001000 Computer Vision (2016 Spring)

Course Information
  • Office Hour 
    • Gunhee Kim: Mon 02:00~03:30 PM
    • Youngjae Yu: TBD
    • Juyong Kim: TBD


  • Five homework assignments (70%), Final term project (30%), Attendance (10% Extra). No midterm and final exam
  • Final grads will be assigned based on the earned scores. A: 3-40%, B: 4-50%, C or below: 20% (subject to change according to the University's rule). 
  • Assignments will be posted via ETL
  • If you have any homework questions, please use the board of ETL.
  • Students are allowed to discuss with others about the problems, but must hand in their own answers. 
  • Every student is allowed 3 total homework late days without penalty for the entire semester. 
    • The deadline of homework is at the beginning of class on the due date.
    • After the deadline, any delay less than 24 hours will accrue to the use of 1 late day.
    • After using up all late days, your credits will be half every 1 day.
  • The objective is to apply the techniques learned in this course to your own research or real-world problems. 
  • Projects should be done in teams of 3~4 students. 
  • In order to earn full scores, projects should submit 3 deliverables: proposal (20%), final report (50%), and presentation (30%).
  • See here for the details.


Lecture Contents


3/2  Introduction to Computer Vision

3/7 Basics of Image Processing
3/9 Frequency Domain Analysis HW1 Out (3/9)
3/14 Image Pyramid
3/16 Edge Detection
3/21 Hough Transform
3/23 Appearance HW1 Due (3/23 11:59PM)
3/28 Photometric Stereo HW2 Out
3/30 Camera I
4/4 Camera II
4/6  Image Alignment
4/11 Image Warping HW2 Due (4/11 23:59PM)
HW3 Out
4/18 Stereo I
4/20 Stereo II
4/25 Optical Flow I
4/27 Optical Flow II HW3 Due (4/27 23:59PM
HW4 Out
5/2 Clustering
5/4 Segmentation
5/9 Image Descriptors
5/11 Image Categorization  HW4 Due (5/11 23:59PM)
5/16 Classification HW5 Out (5/16)
5/18  Object Detection
5/23 Neural Networks 
5/25 Convolutional Neural Networks
5/30 Recurrent Neural Networks HW5 Due (5/30 23:59PM)
6/1 PCA and Face Recognition
6/6 Review  
6/8 Project Presentation

  • This course is based on 15-385 Computer Vision that the instructor TA'ed in CMU. We are extremely grateful to the researchers who allow us to use or modify their slides for this course: Srinivasa Narasimhan.
  • Please do also acknowledge the original sources where appropriate.