stepEdgeParSel {DRIP} | R Documentation |
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
|
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, doi:10.1080/00401706.2013.844732.
See Also
Examples
set.seed(24)
parSel <- stepEdgeParSel(image = sar, bandwidth = 5,
thresh = c(17, 21), nboot = 1)