Introduction to Computer Vision, Spring 2017
Course number: Info I400 / CS B457
Meets: Tuesday/Thursday 2:30-3:45pm
Location: Info W 107 / Info W 109 (lab)
Instructor: Prof. Michael S. Ryoo
Email: mryoo "at" indiana.edu
Office: Informatics E259
Office hours: Friday 1-2pm
Associate Instructor (i.e., teaching assistant): Shujon Naha
Email: snaha "at" umail.iu.edu
Location: Sitting area of Info West
Office hours: Wednesday 1:15pm-2:15pm
*Announcement* Check the last year’s course website to get a better understanding of the course: link
Computer Vision is the study of enabling machines to "see" the visual world (i.e., understand images and videos). In this course, the students will learn fundamental computer vision algorithms and have opportunities to implement them. The course will have a format of half-lecture-half-lab, meaning that there will be one lecture (1.5 hours) per week to study the theory and one lab experience (1.5 hours) to implement the learned techniques. The basic machine learning frameworks necessary for automated understanding of images and videos will also be discussed, while particularly focusing on object recognition from images and activity recognition from videos.
The topics to be covered include:
Computer Vision: Algorithms and Applications by Richard Szeliski
Programming Computer Vision with Python by Jan Erik Solem
(Optional) Visual Object Recognition, by Grauman and Liebe
Interest in computer vision; basic programming skills; basic knowledge of probability, linear algebra, data structures, and algorithms
Features and filters
Szeliski 3.1.1, 3.1.2, 3.2
Szeliski 3.2.3, 4.2
Edges, Texture, Color
Szeliski 3.3.2, 3.3.3, 3.3.4, 2.3.2, 10.5
Grouping and fitting
Grauman&Liebe 7, 8, 9.1, 11.1
Local features [ppt]
Category recognition [ppt]
Videos - motion and tracking
Szeliski 8.4, Solem 10.4
Optical flows [ppt]
Szeliski 8.4, 12.6.4
Human activity analysis: A review, ACM Computing Surveys, 43(3), 2011
Activity recognition [ppt]
Deep learning [ppt]
Course requirements and grading:
Programming assignments (50%): the students will work on implementations of the learned computer vision techniques. This will be done during the lab and through the homeworks.
Midterm exam (25%): the midterm exam.
FInal exam (25%): the final exam.
This course has been inspired by the Computer Vision course by Kristen Grauman (UT), Devi Parikh (Virginia Tech), and Yong Jae Lee (UC Davis).