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.

Prerequisites

- • "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)

Teaching Assistants

- Kumar Shaurya Shankar,
*kumarsha@andrew.cmu.edu* - Chen Kong,
*chenk@cs.cmu.edu* - Prakruti Catherine Gogia,
*pgogia@andrew.cmu.edu* - Animesh Ramesh,
*animesh1@andrew.cmu.edu*

Discussion

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.

Schedule

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? |
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Jan 18 | ||

2. Image Processing |
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Jan 30 | ||

Feb 1 | ||

3. Hough Transform |
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Feb 6 | ||

4. Corner Detection |
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Feb 8 | ||

5. Visual Recognition |
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Feb 13 | ||

Feb 15 | ||

6. Convolutional Neural Networks |
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Feb 22 | ||

Feb 27 | ||

7. 2D Transforms |
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Mar 1 | ||

Mar 6 | ||

8. Multi-View Geometry |
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Mar 8 | ||

Mar 20 | ||

9. Structure From Motion |
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Mar 22 | ||

Apr 3 | ||

10. Stereo |
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Apr 5 | ||

11. Optical Flow |
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Apr 10 | ||

Apr 12 | ||

12. Tracking |
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Apr 17 | ||

13. Filtering |
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Apr 24 | ||

Apr 26 | ||

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