CIS580: Machine Perception

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Spring 2018

Dr. Kostas Daniilidis

Teaching Assistants:
Anand Rajaraman
Office Hours: Wednesday 2:00 PM - 4:00 PM | Levine 4th Floor GRASP Conference Room

Carlos Henrique Machado Silva Esteves
Office Hours: Tuesday 3:00 PM - 5:00 PM | Levine 4th Floor Bump Space

Christine Allen-Blanchette
Office Hours: Monday 10:00 AM - 12:00 PM | Levine 4th Floor Bump Space

Haoyuan Zhang
Office Hours: Monday 5:00 PM - 6:00 PM, Friday 2:00 PM - 3:00 PM | Levine 4th Floor GRASP Conference Room

Rachit Bhargava
Office Hours: Tuesday 10:00 AM - 12:00 PM | Levine 4th Floor GRASP Conference Room

Vivek Venkatram
Office Hours: Thursday 4:00 PM - 6:00 PM | Levine 4th Floor Bump Space

Skirkanich Hall

Spring 2018, Monday & Wednesday, 12:00 PM - 1:30 PM

(optional) Computer Vision: Algorithms and Applications by Richard Szeliski
(optional) Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman



Grading Policy:
Homework: 60%, Midterm 1: 20%, Midterm 2: 20%
There will be six homeworks, three on Signal Processing and three on Projective Geometry.
The first midterm will be held on Signal Processsing before Spring Break.
The second midterm will be held on Projective Geometry during Final Examinations.

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.

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.

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.