Term: Spring 2019
Time: Monday & Wednesday, 12:00 PM - 1:30 PM
Dr. Kostas Daniilidis
Email: email@example.com Office Hours: Wed 1:30-3p
Office: 472 Levine Hall
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.
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 Feb 27. The second midterm will be held on Image Processing and Deep Learning on May 7th at 9a (room TBD).
Late Policy: 10% reduction per day on the assignment. Maximum of 7 days late on each assignment.
Office Hour: Th 5-7 pm, Levine 4th bump space
Office Hour: Mon 1:30-3:30pm, Levine 4th bump space
Office Hour: Th 10am-12pm, Levine 4th bump space
Office Hour: Wed 4-6 pm, Levine 4th Bump space
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.