aro.smooth.tool: aro.smooth.tool
In Arothron/ctrlR: Functions used in my PhD thesis

Description Usage Arguments Value Author(s) Examples

This function find the optimal smoothing algorithm setting giving a mesh a landmark set and a semi-landmark set.

aro.smooth.tool(model, SL.set, L.set, algorithms = c("taubin", "angweight",
  "fujilaplace", "laplace", "hclaplace"), iter = 10, tarface,
  deltaFJ = NULL, deltaAW = NULL, lambda = c(0.01, 0.05, 0.1, 0.15, 0.2),
  tau = 0.01, lambda.levels = 5, deltaFJ.levels = 5, deltaAW.levels = 5,
  lambda.start = 0.95, deltaFJ.start = 0.95, deltaAW.start = 0.95,
  lambda.f = 0.99, deltaFJ.f = 0.99, deltaAW.f = 0.99, lambda.iter = 70,
  l.lambda.iter = 7, deltaFJ.iter = 70, l.deltaFJ.iter = 7,
  deltaAW.iter = 70, l.deltaAW.iter = 7, num.cores = NULL,
  iter_scale_tau = 1, iter_scale_fuj = 1, iter_scale_ang = 1)

`model`	triangular mesh stored as object of class "mesh3d"
`SL.set`	character: kxm matrix semi-landmark set
`L.set`	character: kxm matrix landmark set
`algorithms`	character: algorithm types stored in Morpho::vcgSmooth
`iter`	numeric: number of smoothing iterations (raccomended no more than 10 iteration)
`tarface`	numeric: target of triangle number
`deltaFJ`	numeric: setting values for deltaFJ (if NULL automatic estimation well be done)
`deltaAW`	numeric: setting values for deltaAW (if NULL automatic estimation well be done)
`lambda`	numeric: setting values for lambda (if NULL automatic estimation well be done)
`tau`	numeric: mu value for "taubin" algorithm
`lambda.levels`	numeric: length range lambda
`deltaFJ.levels`	numeric: length range deltaFJ
`deltaAW.levels`	numeric: length range deltaAW
`lambda.start`	numeric: upper value lambda smoothing range
`deltaFJ.start`	numeric: upper value deltaFJ smoothing range
`deltaAW.start`	numeric: upper value deltaAW smoothing range
`lambda.f`	numeric: factor for estimation lambda
`deltaFJ.f`	numeric: factor for estimation deltaFJ
`deltaAW.f`	numeric: factor for estimation deltaAW
`lambda.iter`	numeric: iteration for lambda estimation
`l.lambda.iter`	numeric: interval for lambda.iter
`deltaFJ.iter`	numeric: iteration for deltaFJ estimation
`l.deltaFJ.iter`	numeric: interval for deltaFJ estimation
`deltaAW.iter`	numeric: iteration for deltaAW estimation
`l.deltaAW.iter`	numeric: interval for deltaAW estimation
`num.cores`	numeric: number of CPUs cores, if NULL use the number of physical CPUs/cores
`iter_scale_tau`	numeric: iter used in scale factor estimation for taubin algorithm
`iter_scale_fuj`	numeric: iter used in scale factor estimation for fujilaplace algorithm
`iter_scale_ang`	numeric: iter used in scale factor estimation for angweight algorithm

matrix_result matrix: matrix with all result (loss/retrieval of anatomical information) for smoothing setting iteration

CS numeric vector: semi-landmark centroid size values in starting, decimated, and best smoothed surfaces

L_set numeric: matrix for landmark set of best smoothed surface

SL_set numeric: matrix for landmark set of best smoothed surface

mesh best smoothed mesh

Antonio Profico, Alessio Veneziano, Alessandro Lanteri, Paolo Piras

## Not run: 
#load the example 1: mesh, and L set
data(exp.dog.mesh)
data(exp.dog.SLset)
data(exp.dog.Lset)
example=aro.smooth.tool(model=dog.mesh,SL.set=dog.SLset,L.set=dog.Lset,iter=10,tarface=2000)

## End(Not run)

## Not run: 
load the example 2: mesh, and L set
data(exp.teeth.mesh)
data(exp.teeth.SLset)
data(exp.teeth.Lset)
example=aro.smooth.tool(model=teeth.mesh,SL.set=teeth.SLset,L.set=teeth.Lset,iter=10,tarface=10000,lambda.iter = 350,l.lambda.iter=20,deltaFJ.iter = 350,l.deltaFJ.iter=20,deltaAW.iter = 350,l.deltaAW.iter=20)

## End(Not run)
## Not run: 
load the example 3: mesh, and L set
data(exp.SCP1.mesh)
data(exp.SCP1.SLset)
data(exp.SCP1.Lset)
example=aro.smooth.tool(model=SCP1.mesh,SL.set=SCP1.SLset,L.set=SCP1.Lset,iter=10,tarface=10000,lambda.iter = 150,l.lambda.iter=20,deltaFJ.iter = 150,l.deltaFJ.iter=20,deltaAW.iter = 150,l.deltaAW.iter=20)

## End(Not run)