CS 410/510: Introduction to Computer Vision

Instructor: Feng Liu

TA: Hoang Le
Office: FAB 120-08     Office: Fishbowl
Office Hours: TR 15:30-16:30 Office Hours: TR 14:30-15:30
Email: fliu@cs.pdx.edu Email: hoanl@pdx.edu


This new course will provide an introduction to computer vision. It will cover algorithms in computer vision and image/video processing. It will focus on the development of visual computing applications using off-the-shelf computer vision libraries, such as OpenCV.
General Course Information
  • Pre-Requisites: CS 202 or its equivalence
  • Schedule: TR 16:40-18:30, Room: UTS 206
  • Syllabus
  • Grading policy: Homework (60%) + Project (40%)
    a. No late homework or project assignment will be accepted for this class.
    b. Online source code cannot be used for homework assignments and project without an explicit permission.
    c. This class enforces the PSU Student Conduct Code.
  • Mailing lists: course-cs-410-054-201901-group@pdx.edu and course-cs-510-057-201901-group@pdx.edu

We do not require any textbooks for this class. But the following two books will be very helpful.

Computer Vision: Algorithms and Applications, by R. Szeliski

This book is available online. Please download its latest version.

Learning OpenCV: Computer Vision with the OpenCV Library,
by Gary Bradski and Adrian Kaehler.

This book provides not only a good reference to the OpenCV library, but also gives very good descriptions of a vast amount of vision algorithms.

Acknowledgement: Many of the lecture notes were modified or taken from materials for similar classes taught at other universities by Professor Lazebnik, Svetlana, Yung-Yu Chuang, Fr¨¦do Durand,  Alexei Efros, Chuck Dyer, Marc Levoy, Steve Seitz, Li Zhang, and Dr. Stephen Chenney and Richard Szeliski. Without their generous help, this class could not have been developed.
Date Topic Notes *Readings
W10: 03/14 Project presentation  Schedule  
W10: 03/12 Project presentation  Schedule  
W9: 03/07 Face detection Notes OpenCV Book: Chapter 13
Szeliski Book: Chapter 14
W9: 03/05 Image classification
Visual saliency 
W8: 02/28 Machine learning approach to object recognition
W8: 02/26 Object recognition: overview Notes
W7: 02/21 Structure from motion Notes OpenCV Book: Chapter 12.1, 12.2, 12.5
or Szeliski Book: Chapter 7.3, 7.4
W7: 02/19 Epipolar geometry Notes Szeliski Book: Chapter 7.1, 7.2
W6: 02/14 3D reconstruction
Homework 3 Q&A
  OpenCV Book: Chapter 11.1-11.4
or Szeliski Book: Chapter 6.2, 6.3
W6: 02/12 Camera calibration Notes OpenCV Book: Chapter 11.1-11.4
or Szeliski Book: Chapter 6.2, 6.3
W5: 02/07 Motion estimation Notes OpenCV Book: Chapter 10.1-10.5
or Szeliski Book: Chapter 4.1.4, 8.4
W5: 02/05 Image warping Notes Szeliski Book: Chapter 3.6
W4: 01/31 Image alignment Notes Szeliski Book: Chapter 6.1
W4: 01/29 Hough transform Notes OpenCV Book: Chapter 6.6
Szeliski Book: Chapter 4.3.1-4.3.2,  6.1.4
W3: 01/24 Fitting Notes
W3: 01/22 Scale-invariant feature detection Notes OpenCV Book: Chapter 10.2-10.4
or Szeliski Book: Chapter 4.1.1-4.1.3
W2: 01/17 Corner detection Notes
W2: 01/15 Filter
Edge detection
Notes OpenCV Book: Chapter 6.1-6.5
or Szeliski Book: Chapter 4.2
W1: 01/10 Filter Notes OpenCV Book: Chapter 5.2
Szeliski Book: Chapter 3.2
W1: 01/08 Course overview
Image representation
Notes OpenCV Book: Chapter 1, 3
* You might need to be on the PSU network or have an ACM/IEEE account to download some of papers.

Homework Assignments
Programming Resources
1. OpenCV
2. OpenCV-Python Installation
3. Visual Studio, OpenGL, and FLTK tutorial