Welcome to Spring 2020

CIS 580

Term: Spring 2020

Location: Skirkanich Hall, Auditorium

Time: Monday & Wednesday, 12:00 PM - 1:30 PM

Course Instructor

Dr. Kostas Daniilidis

Email: kostas@cis.upenn.edu

Office: 472 Levine Hall

Office Hours: Mon 1:30 - 3 pm Levine 4th Floor Bump Space

Course Description

CIS580 is an introduction to the problems of computer vision and machine perception that can be solved using geometrical approaches rather than statistical methods, with emphasis on analytical and computational techniques. This course is designed to provide students with an exposure to fundamental mathematical and algorithmic techniques that are used to tackle challenging image-based modeling problems. The content of this course finds application in the fields of Artifical Intelligence and Robotics. Some of the topics that are covered are: Signal processing, projective geometry, camera calibration, image formation and transformations, computational stereopsis, and structure from motion.

Prerequisites: No prior experience with computer vision is assumed, however the following skills are necessary for this class: Mathematics (Linear algebra, vector calculus, and probability), data structures (representing images as features and geometric constructions) and programming.

Textbook References:

Computer Vision: Algorithms and Applications by Richard Szeliski (optional)

Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman (optional)

Grading Policy:

Homework: 60%, Midterm 1: 20%, Midterm 2: 20%

Originally we were planning on 6 homeworks. We decided to break them into shorter pieces . The first midterm will be held on Projective Geometry Wed March 4th. The second midterm will be held on Image Processing and Deep Learning on last day of classes.

Late Policy: 10% reduction per day on the assignment. Maximum of 7 days late on each assignment.


Piazza: piazza.com/upenn/spring2020/srs_cis5800012020a


Teaching Assistants

Weiyu Du

Email: weiyudu@seas.upenn.edu

Office Hour: Mon 8 - 10 pm

Link: Here

Adarsh Modh

Email: adarshm@seas.upenn.edu

Office Hour: Thur 5 - 7 pm

Link: Here

Kendall Queen

Email: queen@seas.upenn.edu

Office Hour: Tues 4:30 - 6:30 pm

Link: Here

Oleh Rybkin

Email: oleh@seas.upenn.edu

Office Hour: Mon 3:00 - 5:00 pm

Link: Here

Karl Schmeckpeper

Email: karls@seas.upenn.edu

Office Hour: Friday 8:00 - 10:00 am

Link: Here

Yufu Wang

Email: yufu@seas.upenn.edu

Office Hour: Wed 2:00 - 4:00 pm

Link: Here

Yuzhi Wang

Email: wangyzh@seas.upenn.edu

Office Hour: Fri 3:00pm - 5:00 pm

Link: Here

Yinshuang Xu

Email: xuyin@seas.upenn.edu

Office Hour: Wed 8:00-10:00 pm

Link: Here

Code of Academic Integrity

University of Pennsylvania's CIS department encourages collaboration among graduate students. However, it is important to recognize the distinction between collaboration and cheating, which is prohibited and carries serious consequences. Cheating may be defined as using or attempting to use unauthorized assistance, material, or study aids in academic work or examinations. Some examples of cheating are: collaborating on a take-home exam or homework unless explicitly allowed; copying homework; handing in someone else's work as your own; and plagiarism. Any student suspected of cheating will be reported to the Office of Student Conduct.