1. Geometric Optimization Problems in Computer Vision

2. X x1 x2 x3

6. Computation of the Fundamental Matrix

13. b Ax Span(A) O

16. 1D Gauss-Newton (Newton) iteration.

17. 1D Gauss-Newton (Newton) iteration (failure)

24. x0x0 x1x1 x2x2 First step minimizes on line. Second step minimizes function in the plane.

30. X0X0

32. Subdivision search

40. Gradient Descent

41. Conjugate Gradient

42. Newton

43. Levenberg-Marquardt

44. Gauss-Newton (without line search)

45. Conjugate gradient Gradient descent Newton Model 1

46. Conjugate gradientGauss-NewtonGradient descent LevenbergNewton Model 2

47. Conjugate gradientGauss-NewtonGradient descent LevenbergNewton Model 3

48. Conjugate gradientGauss-NewtonGradient descent LevenbergNewton Model 4

49. Conjugate gradientGauss-NewtonGradient descent LevenbergNewton Model 5

50. Conjugate gradientGauss-NewtonGradient descent LevenbergNewton Model 6

52. Bundle-adjustment

83. Robust line estimation - RANSAC Fit a line to 2D data containing outliers There are two problems 1.a line fit which minimizes perpendicular distance 2.a classification into inliers (valid points) and outliers Solution: use robust statistical estimation algorithm RANSAC (RANdom Sample Consensus) [Fishler & Bolles, 1981]

84. Repeat 1.Select random sample of 2 points 2.Compute the line through these points 3.Measure support (number of points within threshold distance of the line) Choose the line with the largest number of inliers –Compute least squares fit of line to inliers (regression) RANSAC robust line estimation

94. Repeat 1.Select random sample of 7 correspondences 2.Compute F (1 or 3 solutions) 3.Measure support (number of inliers within threshold distance of epipolar line) Choose the F with the largest number of inliers Algorithm summary – RANSAC robust F estimation

95. Correlation matching results Many wrong matches (10-50%), but enough to compute F

96. Correspondences consistent with epipolar geometry

97. Computed epipolar geometry

120. h