diffLL2K: local linear kernel difference

Description Usage Arguments Details Value References See Also Examples

View source: R/diffLL2K.r

Description

Compute difference between two one-sided LL2K estimators along the gradient direction.

Usage

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diffLL2K(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 linear kernel (LL2K) 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, diffLC2K, diffLLK, stepEdgeLL2K

Examples

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data(sar) # SAR image is bundled with the package and it is a 
          # standard test image in statistics literature.
diff = diffLL2K(image = sar, bandwidth = 6)

DRIP documentation built on May 1, 2019, 7:14 p.m.

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