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