http://www.cse.psu.edu/~rtc12/CSE486/
https://stackoverflow.com/questions/10163034/how-can-i-calculate-camera-position-by-comparing-two-photographs
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.
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 |