stepEdgeLCK: Edge detection, denoising and deblurring

Description Usage Arguments Details Value References See Also Examples

View source: R/stepEdgeLCK.r

Description

Detect step edges in an image using piecewise local constant kernel smoothing.

Usage

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stepEdgeLCK(image, bandwidth, thresh, 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.

thresh

Threshold value used in the edge detection criterion.

plot

If plot = TRUE, an image of detected edges 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 constant kernel (LCK) estimates are obtained in the two half neighborhoods respectively. The pixel is flagged as a step edge pixel if |\widehat{f}_+ - \widehat{f}_-|>u, where u is a threshold value.

Value

Returns a matrix of zeros and ones of the same size as image. Value one represent edge pixels and value zero represent non-edge pixels.

References

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

See Also

stepEdgeLC2K, stepEdgeLLK, stepEdgeLL2K, diffLCK

Examples

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data(sar) # SAR image is bundled with the package and it is a 
          # standard test image in statistics literature.
edge = stepEdgeLCK(image = sar, bandwidth = 4, thresh = 20)

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

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