CVFX Lecture 12 Parametric Transformations and Scattered Data Interpolation

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ECSE-6969 Computer Vision for Visual Effects • Rich Radke, Rensselaer Polytechnic Institute • Lecture 12: Parametric Transformations and Scattered Data Interpolation (3/3/14) • 0:00:01 Computer Vision for Visual Effects • 0:00:43 Dense correspondence vs. feature matching • 0:01:51 Motion vectors • 0:05:40 Parametric transformations • 0:06:11 Translation • 0:06:31 Rotation • 0:06:59 Similarity transformations • 0:08:03 Shears • 0:09:40 Affine transformations • 0:10:50 Projective transformations • 0:13:51 Estimating projective transformations • 0:18:33 Pre-normalizing correspondences • 0:19:59 The Direct Linear Transform (DLT) • 0:21:29 Outlier rejection • 0:25:59 Scattered data interpolation • 0:26:50 Bilinear interpolation • 0:28:57 Thin-plate spline interpolation • 0:38:00 Thin-plate interpolation example • 0:44:27 B-spline interpolation • 0:45:50 Diffeomorphic transformations • Follows Sections 5.1-5.2 of the textbook. http://cvfxbook.com • Key references: • R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2nd edition, 2004. • http://www.robots.ox.ac.uk:5000/~vgg/... • F. Bookstein. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567--85, June 1989. • http://dx.doi.org/10.1109/34.24792 • S. Joshi and M. Miller. Landmark matching via large deformation diffeomorphisms. IEEE Transactions on Image Processing, 9(8):1357--70, Aug. 2000. • http://dx.doi.org/10.1109/83.855431

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