CGGM.mean {epsi}R Documentation

Corner-Preserving Smoothing

Description

Computation of two dimensional redescending M-kernel estimators.

Usage

  CGGM.mean(data, h, g=NULL, silent=FALSE)

  CGGM.lts(data, h, g=NULL, trim=0, silent=FALSE)

  CGGM.autoscale(data, h, silent=FALSE)

Arguments

data numerical matrix of observed data.
h positive number. Bandwidth for the kernel.
g optional positive number. Scale parameter. If g is NULL (default), the scale parameter is determined automatically by the function CGGM.autoscale.
trim optional number within [0,1). Part of observations trimmed by CGGM.lts
silent optional boolean. If true, CGGM.autoscale produces no output.

Details

CGGM.mean implements a corner-preserving smoothing method introduced by Chu et al. (1998) which is based on a redescending M-kernel estimator. As kernel and score function the density of the standard normal distribution is used. A robust version of this estimator is introduced by Hillebrand (2002) and implemented in CGGM.lts.

CGGM.autoscale calculates the median of the interquartile ranges within the 'windows' used in CGGM.mean and CGGM.lts. This can be used as scale parameter.

Value

Return value is a numerical matrix containing the smoothed data.

Author(s)

Tim Garlipp (garlipp@mathematik.uni-oldenburg.de), Martin Hillebrand (Martin.Hillebrand@gmx.de)

References

Chu, C.K., Glad, I.K., Godtliebsen, F., Marron, J.S. (1998) Edge-Preserving Smoothers for Image Processing, J. Amer. Statis. Assoc. 93, 526-541.

Hillebrand, M. (2002) On Robust Corner-Preserving Smoothing in Image Processing, Carl-von-Ossietzky-Universität Oldenburg, Dissertation (http://docserver.bis.uni-oldenburg.de/publikationen/dissertation/2003/hilonr03/hilonr03.html).

Examples


  y <- matrix(rep(0,60*60),nrow=60)
  y[21:40,21:40]<-1
  y <- y + matrix(rnorm(60*60,0,0.1),nrow=60)
  image(y,col=gray(seq(0,1,1/255)))

  ymean <- CGGM.mean(y,0.04)
  image(ymean,col=gray(seq(0,1,1/255)))

[Package epsi version 1.0-3 Index]