stepEdgeParSel: Parameter Selection in Step Edge Detection

In DRIP: Discontinuous Regression and Image Processing

Parameter Selection in Step Edge Detection

Description

Select the bandwidth and threshold parameters for step edge detection.

Usage

stepEdgeParSel(image, bandwidth, thresh, nboot, degree = 1,
blur = FALSE)

Arguments

image	A square matrix, no missing value allowed.
bandwidth	A positive integer that specifies the number of pixels to use in the local smoothing.
thresh	The threshold value to use in the edge detection criterion. Must be a positive value.
nboot	Number of bootstrap samples to use in estimating d_{KQ}.
degree	An integer equal to 0 for local constant kernel smoothing or 1 for local linear kernel smoothing. The default value is 1.
blur	If blur = TRUE, in addition to a conventional 2-D kernel function, a 1-D kernel is used in local smoothing to address the issue of blur. The default value is FALSE.

Details

A jump-preserving local linear kernel smoothing is applied to estimate the discontinuous regression surface; Bootstrap samples are obtained by drawing with replacement from the residuals and the d_{KQ} is computed for the detected edges of the original sample and those of the bootstrap samples.

Value

A list of the selected bandwidth, the selected threshold value and a matrix of d_{KQ} values with each entry corresponding to each combination of bandwidth and threshold.

References

Kang, Y. and Qiu, P. (2014) "Jump Detection in Blurred Regression Surfaces," Technometrics, 56(4), 539 – 550, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2013.844732")}.

See Also

roofDiff, stepDiff, roofEdge

Examples

set.seed(24)
parSel <- stepEdgeParSel(image = sar, bandwidth = 5,
thresh = c(17, 21), nboot = 1)

DRIP documentation built on May 29, 2024, 4:56 a.m.