DRIP (version 1.6)

diffLCK: local constant kernel difference

Description

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

Usage

diffLCK(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 local constant kernel (LCK) 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

diffLLK, diffLC2K, diffLL2K, stepEdgeLCK

Examples

# NOT RUN {
data(sar) # SAR image is bundled with the package and it is a 
          # standard test image in statistics literature.
diff = diffLCK(image = sar, bandwidth = 4)
# }