diffLC2K: local constant kernel difference

In DRIP: Discontinuous Regression and Image Processing

local constant kernel difference

Description

Compute difference between two one-sided LC2K estimators along the gradient direction.

Usage

diffLC2K(image, bandwidth, plot)

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 deblurring local constant kernel (LC2K) estimates are obtained in the two half neighborhoods respectively.

Value

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

References

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

See Also

diffLCK, diffLLK, diffLL2K, stepEdgeLC2K

Examples

data(sar) # SAR image is bundled with the package and it is a 
          # standard test image in statistics literature.
diff = diffLC2K(image = sar, bandwidth = 4)

DRIP documentation built on April 7, 2022, 1:06 a.m.