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.

Author(s)

Yicheng Kang

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 April 4, 2025, 12:31 a.m.