gbpt.2d(x, partition, taper = "poly2", n.taper=8, boundary="zero") igbpt.2d(x)
igbpt.2d
only, an object of class
gbpt.2d
.
"boxcar", "poly1", "poly2", "poly3", "poly4", "poly5"
, or
"trig"
.
See the function
bp.table
for details.
2*n.taper
.
"reflect", "periodic"
and
"zero"
.
See the function
bp.table
for details.
gbpt.2d
.
This algorithm is a generalized version of the Bruhslet Packet Transform (See
bpt
). Whereas in
bpt.2d
the only kinds of partitions that can be defined
are dyadic partitions (partitions corresponding to powers of 2), in this
function any kind of partition (not neccesarily dyadic) can be defined.
In case the partitions are dyadic, the resulting
transform will be identical to the Bruhslet Packet Transform.
The algorithms for the taper functions are given in the
S+WAVELETS User's Manual,
in the section "Cosine Packet Algorithms". They are discussed in
greater depth in Wickerhauser (1994).
The default optional arguments
taper, boundary
can be reset using function
wavelet.options
, see
wavelet.options
for
details.
Meyer, F.G. and Coifman, R.R. (1997), Brushlets: A tool for directional image analysis and image compression Applied and Computational Harmonic Analysis, Academic Press Publishers.
Wickerhauser, M. V. (1994). Adapted Wavelet Analysis from Theory to Software. A. K. Peters Ltd, Wellesley, MA.
## compute the generalized bruhslet packet transform for a dyadic partition brain.gbpt <- gbpt.2d(brain, partition=c(256, 512, 768), n.taper=8) ## the above transform is the same as obtained by the following bpt.2d function brain.bpt <- bpt.2d(brain, n.levels=2, n.taper=8, dct.type=4) ## to reconstruct the image apply the igbpt.2d function recon <- igbpt.2d(brain.gbpt) ## we can also compute the gbpt for a non-dyadic partition brain.gbpt <- gbpt(brain, partition=list(c(25, 47, 100, 717, 930),c(50,78,100)), n.taper=8)