16-385 Computer Vision, Spring 2018
Time: Mondays, Wednesdays 1:30PM - 2:50PM
Location: Doherty Hall 1112
Instructor: Ioannis (Yannis) Gkioulekas
Teaching Assistants: Gaurav Mittal, Shashank Tripathi
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.

Prerequisites

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.

Textbook

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:

Evaluation

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 three free late days. Any additional late days will each incur a 10% penalty.

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

Discussion

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: Regular office hours will be as shown below.

Feel free to email us about scheduling additional office hours.

Discussion: We use Piazza for class discussion and announcements.

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.

DateTopicsSlidesAssignments
W, Jan 17Introductionpdf, pptx
M, Jan 22Image filteringpdf, pptx
W, Jan 24Image pyramids and Fourier transformpdf1/pdf2, pptx/pptx2HW0 and HW1 out
M, Jan 29Hough transformpdf, pptx
W, Jan 31Feature and corner detectionpdf, pptx
M, Feb 5Feature descriptors and matchingpdf, pptx
W, Feb 72D transformationspdf, pptxHW1 due
F, Feb 9HW2 out
M, Feb 12Image homographiespdf, pptx
W, Feb 14Camera modelspdf, pptx
M, Feb 19Two-view geometrypdf, pptx
W, Feb 21Stereopdf, pptx
F, Feb 23HW2 due, HW3 out
M, Feb 26Structured light
W, Feb 28Structure from motion
M, Mar 5Reflectance and image formation
W, Mar 7Photometric stereo and shape from shading
F, Mar 9HW3 due, HW4 out
M, Mar 12No class (spring break)
W, Mar 14No class (spring break)
M, Mar 19Color
W, Mar 21Image processing pipeline
F, Mar 23HW4 due, HW5 out
M, Mar 26Basics of probability
W, Mar 28Faces
M, Apr 2Object recognition
W, Apr 4Neural networks
F, Apr 6HW5 due, HW6 out
M, Apr 9Convolutional neural networks
W, Apr 11Optical flow
M, Apr 16Alignment
W, Apr 18Tracking
F, Apr 20HW6 due, HW7 out
M, Apr 23Clustering
W, Apr 25Segmentation
M, Apr 30Computational photography
W, May 2Internet-based methods
F, Mat 4HW7 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 2017 (Instructor: Kris Kitani)

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

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