Algorithm 1.
PCA using SVD
| for i = 0 : N do |
| P3D(i, 1) ← tip_position_x(i) |
| P3D(i, 2) ← tip_position_y(i) |
| P3D(i, 3) ← tip_position_z(i) |
| end for |
| P̄3D ← P3D−mean(P3D) {subtract the mean value} |
| [U, Σ, V] ←svd(P̄3D) {SVD decomposition} |
| P2D ← UT P3D {project the 3D data} |
| r̂d ← circlefitting(P2D) {Least squares circle fitting} |
| κ̂d ← 1/r̂d |