CV Lecture Notes

There are many excellent sets of course slides available on the web. For example,

(RS) Rick Szeliski's book Computer Vision - Algorithms and Applications
(TD) Trevor Darrell's Computer Vision class at Berkeley
(AT) Antonio Torralba's Advances in Computer Vision class at MIT
(JH) Jame Haye's Introduction to Computer Vision class at Brown
(KG) Kristen Grauman's Computer Vision class at UT Austin
(PF) Pascal Fua's Introduction to Computer Vision class at EPFL
(MP) Marc Pollefey's Multiple View Geometry class at UNC

Please follow the URL to the web site of an indvidual course/book to download the reading material.

The following are links to both new and old slides. Most likely we will not cover all of these topics.

Notes

Course overview

Whirlwind tour of machine learning (pattern recognition)

1-introduction.pdf

2-edge.pdf

3-elink.pdf

4-2Danalysis.pdf

5-coner.pdf

6-stitching.pdf

7-motion2.pdf

8-camera.pdf

9-projection-geometry.pdf

11-stereo.pdf

12-SfM.pdf

13-recognition.pdf

For this course, we will rely mostly on recent papers. My students have compiled lists of papers that might be of interest to you.

More fundamental or "traditional" deep-learning CV papers

More recent papers (mostly CVPR 17)

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