warper {SpatialVx} | R Documentation |
Image Warp
Description
Estimate an image warp
Usage
warper(Im0, Im1, p0, init, s, imethod = "bicubic", lossfun = "Q",
lossfun.args = list(beta = 0, Cmat = NULL), grlossfun = "defaultQ",
lower, upper, verbose = FALSE, ...)
Arguments
Im0 , Im1 |
Numeric matrices giving the zero- and one-energy images. The |
p0 |
nc by 2 matrix giving the zero-energy control points. |
init |
nc by 2 matrix giving an initial estimate of the one-energy control points. |
s |
Two-column matrix giving the full set of locations. Works best if these are integer-valued coordinate indices. |
imethod |
character giving he interpolation method to use. May be one of "round", "bilinear" or "bicubic". |
lossfun |
Function giving the loss function over which to optimize the warp. Default is |
lossfun.args |
A list giving optional arguments to |
grlossfun |
(optional) function giving the gradient of the loss function given by |
lower , upper |
(optional) arguments to the |
verbose |
logical, should progress information be printed to the screen? |
... |
Optional arguments to |
Details
A pair-of-thin-plate-splines image warp is estimated by optimizing a loss function using nlminb. It can be very difficult to get a good estimate. It is suggested, therefore, to obtain good initial estimates for the one-energy control points. The function iwarper
can be useful in this context.
Value
A list object of class “warped” is returned with components:
Im0 , Im1 , Im1.def |
Matrices giving the zero- and one-energy images and the deformed one-energy image, resp. |
p0 , p1 |
zero- and one-energy control points, resp. |
sigma |
Estimated standard error of the mean difference between the zero-energy and deformed one-energy images. |
"warped.locations" "init"
s , imethod , lossfun , lossfun.args |
Same as input arguments. |
theta |
The matrices defining the image warp, L, iL and B, where the last is the bending energy, and the first two are nc + 3 by nc + 3 matrices describing the control points and inverse control-point matrices. |
arguments |
Any arguments passed via ... |
fit |
The output from nlminb. |
proc.time |
The process time. |
Author(s)
Eric Gilleland
References
Dryden, I. L. and K. V. Mardia (1998) Statistical Shape Analysis. Wiley, New York, NY, 347pp.