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CSE/EE486 Computer Vision I

http://www.cse.psu.edu/~rtc12/CSE486/

http://www.cse.psu.edu/~rtc12/CSE486/lecture12.pdf

https://stackoverflow.com/questions/10163034/how-can-i-calculate-camera-position-by-comparing-two-photographs

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

posted on 2017-11-17 13:46 zmj 閱讀(761) 評論(0) 編輯收藏引用

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CSE/EE486 Computer Vision I

Background

Fall 2007 Lecture Notes