EMD (version 1.5.9)

extractimf2d: Bidimensional Intrinsic Mode Function

Description

This function extracts the bidimensional intrinsic mode function from given an image utilizing extrema detection based on the equivalence relation between neighboring pixels.

Usage

extractimf2d(residue, x=NULL, y=NULL, nnrow=nrow(residue), 
    nncol=ncol(residue), tol=sd(c(residue))*0.1^2, 
    max.sift=20, boundary="reflexive", boundperc=0.3,  
    sm="none", spar=NULL, weight=NULL, check=FALSE)

Arguments

residue

matrix of an image observed at (x, y)

x, y

locations of regular grid at which the values in residue are measured

nnrow

the number of row of an input image

nncol

the number of column of an input image

tol

tolerance for stopping rule of sifting

max.sift

the maximum number of sifting

boundary

specifies boundary condition from ``none", ``symmetric" or ``reflexive".

boundperc

expand an image by adding specified percentage of image at the boundary when boundary condition is 'symmetric' or 'reflexive'.

sm

specifies whether envelop is constructed by interpolation, thin-plate smoothing, Kriging, local polynomial smoothing, or loess. Use ``none" for interpolation, ``Tps" for thin-plate smoothing, ``mKrig" for Kriging, ``locfit" for local polynomial smoothing, or ``loess" for loess.

spar

specifies user-supplied smoothing parameter of thin-plate smoothing, Kriging, local polynomial smoothing, or loess.

weight

deprecated.

check

specifies whether the sifting process is displayed. If check=TRUE, click the plotting area to start the next step.

Value

imf

two dimensional IMF

residue

residue signal after extracting the finest IMF from residue

maxindex

index of maxima

minindex

index of minima

niter

number of iteration obtaining the IMF

Details

This function extracts the bidimensional intrinsic mode function from given image utilizing extrema detection based on the equivalence relation between neighboring pixels. See Kim et al. (2012) for detalis. See Kim et al. (2012) for detalis.

References

Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H. (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society London A, 454, 903--995.

Kim, D., Park, M. and Oh, H.-S. (2012) Bidimensional Statistical Empirical Mode Decomposition. IEEE Signal Processing Letters, 19, 191--194, doi: 10.1109/LSP.2012.2186566.

See Also

extrema2dC, emd2d.

Examples

# NOT RUN {
data(lena)
z <- lena[seq(1, 512, by=4), seq(1, 512, by=4)]

# }
# NOT RUN {
lenaimf1 <- extractimf2d(z, check=FALSE)
# }