icpmat: match two landmark configurations using iteratively closest...

In zarquon42b/Morpho: Calculations and Visualisations Related to Geometric Morphometrics

match two landmark configurations using iteratively closest point search

Description

match two landmark configurations using iteratively closest point search

Usage

icpmat(
  x,
  y,
  iterations,
  mindist = 1e+15,
  subsample = NULL,
  type = c("rigid", "similarity", "affine"),
  weights = NULL,
  threads = 1,
  centerweight = FALSE
)

Arguments

x	moving landmarks
y	target landmarks
iterations	integer: number of iterations
mindist	restrict valid points to be within this distance
subsample	use a subsample determined by kmean clusters to speed up computation
type	character: select the transform to be applied, can be "rigid","similarity" or "affine"
weights	vector of length nrow(x) containing weights for each row in x
threads	integer: number of threads to use.
centerweight	logical: if weights are defined and centerweigths=TRUE, the matrix will be centered according to these weights instead of the barycenter.

Value

returns the rotated landmarks

Examples

data(nose)
icp <- icpmat(shortnose.lm,longnose.lm,iterations=10)

## example using weights
## we want to assign high weights to the first three cordinates
icpw <- icpmat(shortnose.lm,longnose.lm,iterations=10,
               weights=c(rep(100,3),rep(1,620)),centerweight = TRUE)
## the RMSE between those four points and the target is now smaller:
require(Rvcg)
RMSE <- sqrt(sum(vcgKDtree(longnose.lm,icp[1:3,],k=1)$distance^2))
RMSEW<- sqrt(sum(vcgKDtree(longnose.lm,icpw[1:3,],k=1)$distance^2))
barplot(c(RMSE,RMSEW),names.arg=c("RMSE weighted","RMSE unweighted"))
## Not run: 
## plot the differences between unweighted and weighted icp
deformGrid3d(icp,icpw)
## plot the first four coordinates from the icps:
spheres3d(icp[1:3,],col="red",radius = 0.5)
spheres3d(icpw[1:3,],col="green",radius = 0.5)
## plot the target
spheres3d(longnose.lm,col="yellow",radius = 0.2)

## End(Not run)
##2D example  using icpmat to determine point correspondences
if (require(shapes)) {
## we scramble rows to show that this is independent of point order
moving <- gorf.dat[sample(1:8),,1]
plot(moving,asp=1) ## starting config
icpgorf <- icpmat(moving,gorf.dat[,,2],iterations = 20)
points(icpgorf,asp = 1,col=2)
points(gorf.dat[,,2],col=3)## target

## get correspondences using nearest neighbour search
index <- mcNNindex(icpgorf,gorf.dat[,,2],k=1,cores=1)
icpsort <- icpgorf[index,]
for (i in 1:8)
lines(rbind(icpsort[i,],gorf.dat[i,,2]))
}

zarquon42b/Morpho documentation built on Jan. 28, 2024, 2:11 p.m.