16-385 Computer Vision, Spring 2017
Instructor: Kris Kitani
Time: Monday, Wednesday 1:30PM - 2:50PM
Location: DOHERTY HALL 1212
Course Description

This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve reallife vision problems.

This course requires familarity with linear algebra and basic probability. MATLAB will be used for project assignments and will be covered as part of the introduction to the course.

Teaching Assistants

We use Piazza for class discussion and announcements.

Special Thanks

These lecture notes have been pieced together from many different people and places. Special thanks to colleagues for sharing their PPTs directly with me: Bob Collins, Srinivasa Narashiman, Martial Hebert, Alyosha Efros, Ali Faharadi. I would also like to thank the following people for making their lecture notes and materials available online: Steve Seitz, Richard Selinsky, Larry Zitnick, Noah Snavely, Lana Lazebnik, Kristen Grauman, Yung-Yu Chuang, T. Tuytelaars, Fei-Fei Li, Antonio Torralba, Rob Fergus, David Claus and Dan Jurafsky.


Slides would be updated on this website 3 days after each lecture. Please see Piazza for latest schedule.

1. Introduction - Why you can succeed in this class?
Jan 18
2. Image Processing
Jan 30
Feb 1
3. Hough Transform
Feb 6
4. Corner Detection
Feb 8
5. Visual Recognition
Feb 13
Feb 15
6. Convolutional Neural Networks
Feb 22
Feb 27
7. 2D Transforms
Mar 1
Mar 6
8. Multi-View Geometry
Mar 8
Mar 20
9. Structure From Motion
Mar 22
Apr 3
10. Stereo
Apr 5
11. Optical Flow
Apr 10
Apr 12
12. Tracking
Apr 17
13. Filtering
Apr 24
Apr 26
Previous Course Websites

16-385 - Computer Vision, Spring 2015

15-385 - Computer Vision, Spring 2014