stepEdgeLLK {DRIP} | R Documentation |
Detect step edges in an image using piecewise local linear kernel smoothing.
stepEdgeLLK(image, bandwidth, thresh, 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. |
thresh |
Threshold value used in the edge detection criterion. |
plot |
If plot = TRUE, an image of detected edges 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. The pixel is flagged as
a step edge pixel if |\widehat{f}_+ - \widehat{f}_-|>u
, where
u
is a threshold value.
Returns a matrix of zeros and ones of the same size as image. Value one represent edge pixels and value zero represent non-edge pixels.
Kang, Y., and Qiu, P., "Jump Detection in Blurred Regression Surfaces," Technometrics, 56, 2014, 539-550.
stepEdgeLCK
, stepEdgeLC2K
, stepEdgeLL2K
,
diffLLK
data(sar) # SAR image is bundled with the package and it is a
# standard test image in statistics literature.
edge = stepEdgeLLK(image = sar, bandwidth = 9, thresh = 17)