CMSC 472/672: Computer Vision

Instructor: Tejas Gokhale (OH: Wednesday 2:30 PM - 3:30 PM or by appointment; ITE 214);
Teaching Assistant: TBD ;
Time: Monday and Wednesday 4:00pm - 5:15pm
Location: ITE 229

This course will offer a comprehensive introduction to the field of computer vision which has the broad goal of understanding visual signals (images and videos) for low/mid/high-level perceptual tasks. This course will introduce fundamental principles and concepts for developing computer vision systems such as image formation, acquisition, and processing, stereo and 3D vision, machine learning algorithms and neural networks for image understanding.

Prerequisites: We will assume that you have a basic (but solid) expertise in linear algebra, geometry, probability, and Python programming. Recommended classes at UMBC are: MATH 221 (Linear Algebra), STAT 355 or CMPE 320 (Probability and Statistics), MATH 151 (Calculus and Analytical Geometry). If you are unfamiliar with linear algebra or calculus, you should consider taking both: without these tools, you are likely to struggle with the course. Although we will provide brief math refreshers of these necessary topics, CMSC 491/691 should not be your first introduction to these topics.
We understand that some students may have had some prior exposure to signal/image/audio processing, computer graphics, machine learning, neural networks etc. However, none of these are pre-requisites -- the class is designed to be self-contained.

Reference Books There are no required textbooks. The following books may be useful to accompany the lectures:

• Computer Vision: Algorithms and Applications by Szeliski. Available for free online.
• Deep Learning by Goodfellow, Bengio, and Courville. Available for free online. See Part I for brushing up on pre-requisities mentioned above.

Schedule is tentative and subject to change.

	Topic	Resources	Additional Reading
1	Introduction
2	Image Formation and Acquisition		Szeliski Ch 2
3	Image Filtering I		Szeliski Ch 3
4	Image Filtering II		Szeliski Ch 3
5	Image Features I		Szeliski Ch 3
6	Image Features II		Torralba Ch 3; SIFT paper
7	Machine Learning for Computer Vision I		Szeliski Ch 5, Goodfellow Ch 5
8	ML-for-CV II (Neural Networks)		Goodfellow Ch 6.1-6.4, Shree Nayar FPCV Playlist
9	ML-for-CV III (Gradient Descent)		Goodfellow Ch 6.5, Roger Grosse: Optimization Notes
10	Visual Recognition		Szeliski Ch 6, Forsyth Ch 18
11	Image Transformations		Szeliski 3.6
12	Homographies		Hartley&Zisserman Part 0 (Ch 2, 3, 4), SVD Tutorial
13	Camera Models		Hartley&Zisserman Part 1 (Ch 6, 7, 8)
14	Triangulation & Epipolar Geometry
15	Stereo Vision
16	Vision-and-Language
17	Image Synthesis
18	Robustness
19	Motion

Homework

• Homework Assignments: 30%
• Midterm Exam: 20%
• Quizzes: 15%
• Project: 35%

Projects

• BS or MS students: I recommend working in groups of 4 students.
• PhD students: If you'd like to work in smaller groups, consult with Tejas during Office Hours and discuss your existing research agenda. We will integrate the course project into that agenda if possible. Group sizes (or individual projects) will be decided on a case-by-case basis.

• Proposal: Clearly state the following:
  • Problem you wish to tackle (and why)
  • Proposed approach and methods
  • Timeline
  • What each student in the group will do.
  • Expected Outcome and Worst-Case Outcome

Late Submission Policy

Academic Integrity