diffLLK: local linear kernel difference

Description Usage Arguments Details Value References See Also Examples

View source: R/diffLLK.r

Description

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

Usage

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diffLLK(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 local linear kernel (LLK) 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, diffLL2K, stepEdgeLLK

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 = diffLLK(image = sar, bandwidth = 6)

DRIP documentation built on Oct. 23, 2020, 6:06 p.m.

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