Description Usage Arguments Details Value References See Also Examples

Compute difference between two one-sided LLK estimators along the gradient direction.

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`image` |
A square matrix object of size n by n, no missing value allowed. |

`bandwidth` |
A positive integer to specify the number of pixels used in the local smoothing. |

`plot` |
If plot = TRUE, an image of the difference at each pixel is plotted. |

At each pixel, the gradient is estimated by a local linear
kernel smoothing procedure. Next, the local neighborhood is
divided into two halves along the direction perpendicular to
(*\widehat{f}'_{x}*, *\widehat{f}'_{y}*). Then the one-
sided local linear kernel (LLK) estimates are obtained in the
two half neighborhoods respectively.

Returns a matrix of the estimated difference, *|\widehat{f}_+ - \widehat{f}_-|*,
at each pixel.

Kang, Y., and Qiu, P., "Jump Detection in Blurred Regression
Surfaces," *Technometrics*, **56**, 2014, 539-550.

`diffLCK`

, `diffLC2K`

, `diffLL2K`

,
`stepEdgeLLK`

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