16-385 Computer Vision, Spring 2019
Time: Mondays, Wednesdays 1:30PM - 2:50PM
Location: Hamerschlag Hall B103
Instructor: Ioannis (Yannis) Gkioulekas
Teaching Assistants: Sharvani Chandu, Chengqian Che, Abhay Gupta, Anshuman Majumdar, Neeraj Sajjan
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 real-life vision problems.


This course requires familarity with linear algebra, calculus, basic probability, as well as programming. In particular, the following courses serve as prerequisite:

Matlab will be used for project assignments and will be covered as part of the introduction to the course.


Readings will be assigned from the following textbook (available online for free):

Additional readings will be assigned from relevant papers. Readings will be posted at the last slide of each lecture.

The following textbooks can also be useful references but are not required:


Your final grade will be made up from:

Homework assignments: All homework assignments will have a programming component, and some of them will also have a theory component involving pen-and-paper exercises. The programming component of all assignments be done in Matlab. There will be an ungraded zeroth assignment that will serve as a short Matlab tutorial. We have collected a few useful Matlab resources here.

Late days: For the homework assignments, students will be allowed a total of five free late days. Any additional late days will each incur a 10% penalty.

Submitting homeworks: We use Canvas for submitting and grading homeworks.


We use Piazza for class discussion and announcements.

Email, Office Hours, and Discussion

Email: Please use [16385] in the title when emailing the teaching staff!

Office hours: Office hours for the rest of the semester are as follows:

Feel free to email us about scheduling additional office hours.

Syllabus and Schedule

The following syllabus is tentative and will most likely change during the semester. Slides will be updated on this site after each lecture.

M, Jan 14Introductionpdf, pptxHW0 out
W, Jan 16Image filteringpdf, pptx
M, Jan 21No class (MLK day)
W, Jan 23Image pyramids and Fourier transformpdf, pptxHW1 out
M, Jan 28No class
W, Jan 30No class
M, Feb 4Hough transformpdf, pptx
W, Feb 6Feature and corner detectionpdf, pptx
M, Feb 11Feature descriptors and matchingpdf, pptx
W, Feb 132D transformationspdf, pptxHW1 due, HW2 out
M, Feb 18Image homographiespdf, pptx
W, Feb 20Camera modelspdf, pptx
M, Feb 25Two-view geometrypdf, pptx
W, Feb 27Stereopdf, pptxHW2 due, HW3 out
M, Mar 4Structure from motionpdf, pptx
W, Mar 6Radiometry and reflectancepdf, pptx
S, Mar 10HW3 due
M, Mar 11No class (spring break)
W, Mar 13No class (spring break)
M, Mar 18Radiometry continuedpdf, pptxHW4 out
W, Mar 20Photometric stereo and shape from shadingpdf, pptx
M, Mar 25Image processing pipelinepdf, pptx
W, Mar 27Introduction to recognitionpdf, pptxHW4 due, HW5 out
M, Apr 1Bag of wordspdf, pptx
W, Mar 3No class
M, Apr 8Neural networkspdf, pptx
W, Apr 10Convolutional neural networkspdf, pptxHW5 due, HW6 out
M, Apr 15Optical flowpdf, pptx
W, Apr 17Alignment and trackingpdf, pptx
M, Apr 22Temporal models and SLAMpdf, pptx
W, Apr 24Graph-based methodspdf, pptxHW6 due, HW7 out
M, Apr 29Segmentationpdf, pptx
W, May 1Wrap-uppdf, pptx
S, May 5HW7 due
Special Thanks

These lecture notes have been pieced together from many different people and places. Special thanks to colleagues for sharing their slides: Kris Kitani, Bob Collins, Srinivasa Narashiman, Martial Hebert, Alyosha Efros, Ali Faharadi, Deva Ramanan, Yaser Sheikh, and Todd Zickler. Many thanks also to 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, Tinne Tuytelaars, Fei-Fei Li, Antonio Torralba, Rob Fergus, David Claus, and Dan Jurafsky.

Previous Course Websites

16-385 - Computer Vision, Spring 2018 (Instructor: Ioannis Gkioulekas)

16-385 - Computer Vision, Spring 2017 (Instructor: Kris Kitani)

16-385 - Computer Vision, Spring 2015 (Instructor: Kris Kitani)

15-385 - Computer Vision, Spring 2014 (Instructor: Srinivasa Narasimhan)