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:

- "Mathematical Foundations of Electrical Engineering" (18-202) and "Principles of Imperative Computation" (15-122) (OR)
- "Matrix Algebra with Applications" (21-240) and "Matrices and Linear Transformations" (21-241) and "Calculus in Three Dimensions" (21-259) and "Principles of Imperative Computation" (15-122)

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):

- Computer Vision: Algorithms and Applications, by Richard Szeliski.

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:

*Multiple View Geometry in Computer Vision,*by Richard Hartley and Andrew Zisserman.*Computer Vision: A Modern Approach,*by David Forsyth and Jean Ponce.*Digital Image Processing,*by Rafael Gonzalez and Richard Woods.

Evaluation

Your final grade will be made up from:

- Seven homework assignments (95%).
- Class participation (5%).

**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.

- Gaurav: Tuesdays 12:00PM - 2:00PM, Smith Hall 2nd floor, graphics lounge.
- Shashank: Thursdays 3:00PM - 5:00PM, Smith Hall 2nd floor, graphics lounge.
- Yannis: Fridays 3:00PM - 5:00PM, Smith Hall 225.

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.

Date | Topics | Slides | Assignments |
---|---|---|---|

W, Jan 17 | Introduction | pdf, pptx | |

M, Jan 22 | Image filtering | pdf, pptx | |

W, Jan 24 | Image pyramids and Fourier transform | pdf1/pdf2, pptx/pptx2 | HW0 and HW1 out |

M, Jan 29 | Hough transform | pdf, pptx | |

W, Jan 31 | Feature and corner detection | pdf, pptx | |

M, Feb 5 | Feature descriptors and matching | pdf, pptx | |

W, Feb 7 | 2D transformations | pdf, pptx | HW1 due |

F, Feb 9 | HW2 out | ||

M, Feb 12 | Image homographies | pdf, pptx | |

W, Feb 14 | Camera models | pdf, pptx | |

M, Feb 19 | Two-view geometry | pdf, pptx | |

W, Feb 21 | Stereo | pdf, pptx | |

F, Feb 23 | HW2 due, HW3 out | ||

M, Feb 26 | Structured light | ||

W, Feb 28 | Structure from motion | ||

M, Mar 5 | Reflectance and image formation | ||

W, Mar 7 | Photometric stereo and shape from shading | ||

F, Mar 9 | HW3 due, HW4 out | ||

M, Mar 12 | No class (spring break) | ||

W, Mar 14 | No class (spring break) | ||

M, Mar 19 | Color | ||

W, Mar 21 | Image processing pipeline | ||

F, Mar 23 | HW4 due, HW5 out | ||

M, Mar 26 | Basics of probability | ||

W, Mar 28 | Faces | ||

M, Apr 2 | Object recognition | ||

W, Apr 4 | Neural networks | ||

F, Apr 6 | HW5 due, HW6 out | ||

M, Apr 9 | Convolutional neural networks | ||

W, Apr 11 | Optical flow | ||

M, Apr 16 | Alignment | ||

W, Apr 18 | Tracking | ||

F, Apr 20 | HW6 due, HW7 out | ||

M, Apr 23 | Clustering | ||

W, Apr 25 | Segmentation | ||

M, Apr 30 | Computational photography | ||

W, May 2 | Internet-based methods | ||

F, Mat 4 | HW7 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.