16-385 Computer Vision, Spring 2020
Time: Mondays, Wednesdays noon - 1:20 pm
Location: Margaret Morrison A14
Instructor: Ioannis (Yannis) Gkioulekas
Teaching Assistants: Anand Bhoraskar, Prakhar Kulshreshtha
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 for different parts of the class, but are not required:


Your final grade will be made up from:

Programming assignments: Programming assignments (PAs) will require implementing a significant computer vision algorithm. Some of them will also have a small theory component relevant to the implementation. Programming will be done in Matlab (PA1) and Python (PA2-7).

Take-home quizzes: Take-home quizzes (TQs) will require solving two-three theory questions related to the corresponding week's two lectures. Answers will need to be typed in LaTeX.

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

Missed quizzes: For the take-home quizzes, students will be allowed to completely skip a total of three quizzes without penalty. For students that submit more than eight quizzes, only the best eight will be counted towards their grade. There are no free late days for quizzes, and any late quiz will receive zero credit.

Submitting homework: 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: All office hours are at the Smith Hall 200 conference room.

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 13Introductionpdf, pptx
W, Jan 15Image filteringpdf, pptx
M, Jan 20No class (Martin Luther King day)
W, Jan 22Image pyramids and Fourier transformpdf, pptxPA1 out
M, Jan 27Hough transformpdf, pptxTQ1 out
W, Jan 28Feature and corner detectionpdf, pptx
M, Feb 3Feature descriptors and matchingpdf, pptxTQ1 due, TQ2 out
W, Feb 52D transformationspdf, pptxPA1 due, PA2 out
M, Feb 102D transformations (continued)pdf, pptxTQ2 due, TQ3 out
W, Feb 12Image homographiespdf, pptx
Su, Feb 16TQ4 out
M, Feb 17Camera modelspdf, pptxTQ3 due
W, Feb 19Camera models (continued)pdf, pptxPA2 due, PA3 out
Su, Feb 23TQ4 due, TQ5 out
M, Feb 24Two-view geometrypdf, pptx
W, Feb 26Stereopdf, pptx
Su, Mar 1TQ5 due, TQ6 out
M, Mar 2Radiometry and reflectancepdf, pptx
W, Mar 4More on radiometrypdf, pptxPA3 due
M, Mar 9No class (spring break)
W, Mar 11No class (spring break)
M, Mar 16No class (Covid-19 transition)
W, Mar 18Photometric stereo and shape from shadingpdf, pptxPA4 out
Su, Mar 22TQ6 due, TQ7 out
M, Mar 23Image processing pipelinepdf, pptx
W, Mar 25Image classificationpdf, pptxPA4 due, PA5 out
Su, Mar 29TQ7 due, TQ8 out
W, Mar 30Bag of workspdf, pptxPA4 due, PA5 out
W, Apr 1Neural networkspdf, pptx
Su, Apr 5TQ9 out
M, Apr 6More neural networkspdf, pptxTQ8 due
W, Apr 8Convolutional neural networkspdf, pptxPA5 due, PA6 out
Su, Apr 12TQ10 out
M, Apr 13More convolutional neural networkspdf, pptxTQ9 due
W, Apr 15Optical flowpdf, pptx
F, Apr 17Alignmentpdf, pptx
Su, Apr 19TQ10 due
M, Apr 20Trackingpdf, pptx
W, Apr 22Segmentation and graph-based techniquespdf, pptxPA6 due, PA7 out, TQ11 out
Su, Apr 26
M, Apr 27Segmentationpdf, pptx
W, Apr 29Structure from motion and wrap-uppdf1/pdf2, pptx1/pptx2
Su, May 3PA7 due, TQ11 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 Szeliski, 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, Fall 2019 (Instructors: Kris Kitani, Srinivasa Narasimhan)

16-385 - Computer Vision, Spring 2019 (Instructor: Ioannis Gkioulekas)

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)