# extractimf2d: Bidimensional Intrinsic Mode Function In EMD: Empirical Mode Decomposition and Hilbert Spectral Analysis

## 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

 ```1 2 3 4``` ```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.

## 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.

## 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

## 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

 ```1 2 3 4 5 6``` ```data(lena) z <- lena[seq(1, 512, by=4), seq(1, 512, by=4)] ## Not run: lenaimf1 <- extractimf2d(z, check=FALSE) ## End(Not run) ```

EMD documentation built on Dec. 5, 2018, 5:04 p.m.