DRIP (version 1.6)

diffLC2K: 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.

Value

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

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.

References

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

See Also

diffLCK, diffLLK, diffLL2K, stepEdgeLC2K

Examples

# NOT RUN {
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)
# }