cpd: cpd

In LOMAR: Localization Microscopy Data Analysis

cpd

Description

Affine and rigid registration of two point sets using the coherent point drift algorithm. See: Myronenko A., Song X. (2010): "Point-Set Registration: Coherent Point Drift", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, issue 12, pp. 2262-2275.

Usage

cpd(
  X,
  Y,
  w = 0,
  weights = NULL,
  scale = FALSE,
  maxIter = 100,
  subsample = NULL,
  tol = 1e-04
)

Arguments

X	reference point set, a N x D matrix
Y	point set to transform, a M x D matrix,
w	noise weight in the range [0, 1)
weights	a M x N matrix of point correspondence weights
scale	logical (default: FALSE), whether to use scaling
maxIter	maximum number of iterations to perform (default: 100)
subsample	if set, use this randomly selected fraction of the points
tol	tolerance for determining convergence

Value

a list of

• Y: transformed point set,

• R: rotation matrix,

• t: translation vector,

• s: scaling factor,

• P: matrix of correspondence probabilities between the two point sets,

• sigma: final variance,

• iter: number of iterations performed,

• converged: boolean, whether the algorithm has converged.

Examples

data.file1 <- system.file("test_data", "parasaurolophusA.txt", package = "LOMAR",
 mustWork = TRUE)
PS1 <- read.csv(data.file1, sep = '\t', header = FALSE)
data.file2 <- system.file("test_data", "parasaurolophusB.txt", package = "LOMAR",
 mustWork = TRUE)
PS2 <- read.csv(data.file2, sep = '\t', header = FALSE)
transformation <- cpd(PS1, PS2, maxIter = 10, tol = 1e-3)
## Not run: 
# Visualize registration outcome
library(rgl)
plot3d(PS1, col = "blue")
points3d(PS2, col = "green")
points3d(transformation[['Y']], col = "magenta")

## End(Not run)

LOMAR documentation built on March 18, 2022, 6:05 p.m.