warper | R Documentation |

Estimate an image warp

```
warper(Im0, Im1, p0, init, s, imethod = "bicubic", lossfun = "Q",
lossfun.args = list(beta = 0, Cmat = NULL), grlossfun = "defaultQ",
lower, upper, verbose = FALSE, ...)
```

`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 |

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.

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. |

Eric Gilleland

Dryden, I. L. and K. V. Mardia (1998) *Statistical Shape Analysis*. Wiley, New York, NY, 347pp.

`iwarper`

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