CVFX Lecture 14: Epipolar geometry

ECSE-6969 Computer Vision for Visual Effects
Rich Radke, Rensselaer Polytechnic Institute
Lecture 14: Epipolar geometry (3/17/14)

0:00:02 Motion vectors and constraints on correspondence
0:02:20 Epipolar geometry
0:12:00 Epipolar lines
0:18:25 Epipoles
0:21:15 Epipolar geometry example
0:29:21 The fundamental matrix F
0:30:46 Getting epipolar lines from F
0:33:07 Getting epipoles from F
0:37:12 Estimating F from correspondences
0:49:03 Rectification
0:52:43 F for rectified images
0:55:27 Rectification algorthms
1:00:17 Rectification example

Follows Section 5.4 of the textbook. http://cvfxbook.com

Key references:

Z. Zhang, R. Deriche, O. Faugeras, and Q.-T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78(1-2):87--119, Oct. 1995.
http://dx.doi.org/10.1016/0004-3702(95)00022-4

R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2nd edition, 2004.
http://www.robots.ox.ac.uk/~vgg/hzbook/

R. Hartley. Theory and practice of projective rectification. International Journal of Computer Vision, 35(2):115--27, Nov. 1999.
http://dx.doi.org/10.1023/A:1008115206617

Видео CVFX Lecture 14: Epipolar geometry канала Rich Radke