Compute difference between two one-sided LLK
estimators along the gradient direction.
Usage
diffLLK(image, bandwidth, plot)
Value
Returns a matrix of the estimated difference, \(|\widehat{f}_+ - \widehat{f}_-|\),
at each pixel.
Arguments
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.
Details
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.
References
Kang, Y., and Qiu, P., "Jump Detection in Blurred Regression
Surfaces," Technometrics, 56, 2014, 539-550.
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)