diffLLK {DRIP} | R Documentation |
Compute difference between two one-sided LLK estimators along the gradient direction.
diffLLK(image, bandwidth, plot)
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
data(sar) # SAR image is bundled with the package and it is a
# standard test image in statistics literature.
diff = diffLLK(image = sar, bandwidth = 6)