CSE/EE486 Computer Vision I
Introduction to Computer Vision
CSE Department, Penn State University
Instructor: Robert Collins

Background

I have taught this course several times (almost every semester). I am always fiddling around with the course content, so the material covered and the order of presentation changes from semester to semester. Below are the lecture notes from Fall 2007.

In addition to slides that I created, I borrowed heavily from other lecturers whose computer vision slides are on the web. I used to put an attribution at the bottom of each slide as to where and who it came from. However, that led to cluttered slides, and was distracting. So, I dropped that format. Instead, I'm telling you up-front that a lot of the slides in the lectures below did not originate from me. Here is a partial list of the main sources that I can remember: Octavia Camps, Forsyth and Ponce, David Jacobs, Steve Seitz, Chuck Dyer, Martial Hebert. If I forgot you, and you see your slides here, well... thanks. And drop me a line so I can add your name to the list.

By the same token, if you are putting together a computer vision course, and want to use some of my slides, go right ahead. You are welcome to them, since the main goal here is to improve the quality of computer vision education everywhere. To quote Thomas Jefferson: "He who receives an idea from me, receives instruction himself without lessening mine; as he who lights his taper at mine, receives light without darkening me. That ideas should freely spread from one to another over the globe, for the moral and mutual instruction of man, and improvement of his condition, seems to have been peculiarly and benevolently designed by nature, when she made them, like fire, expansible over all space, without lessening their density at any point, and like the air in which we breathe, move, and have our physical being, incapable of confinement or exclusive appropriation." Jefferson was one awesome dude.

Fall 2007 Lecture Notes

Detailed List of Topics Covered in Fall 2007

Lecture 01: Intro to Computer Vision slides 6 per page
Lecture 02: Intensity Surfaces and Gradients slides 6 per page
Lecture 03: Linear Operators and Convolution slides 6 per page
Lecture 04: Smoothing slides 6 per page
Lecture 05: Edge Detection slides 6 per page
Lecture 06: Corner Detection slides 6 per page
Lecture 07: Template Matching slides 6 per page
Lecture 08: Introduction to Stereo slides 6 per page
Lecture 09: Stereo Algorithms slides 6 per page
Lecture 10: Image Pyramids slides 6 per page
Lecture 11: LoG Edge and Blob Finding slides 6 per page
Lecture 12: Camera Projection (Extrinsics) slides 6 per page
Lecture 13: Camera Projection (Intrinsics) slides 6 per page
Lecture 14: Parameter Estimation; Image Warping slides 6 per page
Lecture 15: Robust Estimation: RANSAC slides 6 per page
Lecture 16: Planar Homographies slides 6 per page
Lecture 17: Stabilization and Mosaicing slides 6 per page
Lecture 18: Generalized Stereo slides 6 per page
Lecture 19: Essential and Fundamental Matrices slides 6 per page
Lecture 20: The 8-point algorithm slides 6 per page
Lecture 21: Stereo Reconstruction slides 6 per page
Lecture 22: Camera Motion Field slides 6 per page
Lecture 23: Optic Flow slides 6 per page
Lecture 24: Video Change Detection slides 6 per page
Lecture 25: Structure From Motion (SFM) slides 6 per page
Lecture 26: Color and Light slides 6 per page
Lecture 27: Application: Skin Color slides 6 per page
Lecture 28: Intro to Tracking slides 6 per page
Lecture 29: Video Tracking: Mean-shift slides 6 per page
Lecture 30: Video Tracking: Lucas-Kanade slides 6 per page
Lecture 31: Object Recognition : SIFT Keys slides 6 per page
Lecture 32: Object Recognition : PCA / Eigenfaces slides 6 per page