roofEdgeParSel: roof/valley edge detection, parameter selection

In DRIP: Discontinuous Regression and Image Processing

roof/valley edge detection, parameter selection

Description

Select bandwidth and threshold value for the roof/valley edge detector using bootstrap procedure

Usage

roofEdgeParSel(image, bandwidth, thresh, nboot, edge1, blur=FALSE)

Arguments

image	A square matrix object of size n by n, no missing value allowed.
bandwidth	Positive integers to specify the number of pixels used in the local smoothing. These are the bandwidth parameters to be chosen from.
thresh	Threshold values to be chosen from.
nboot	Number of bootstrap samples.
edge1	Step edges. The function excludes step edges when detect roof/valley edges.
blur	TRUE if the image contains blur, FALSE otherwise.

Details

If blur=TRUE, then a conventional local linear kernel smoothing is applied to estimate the blurred 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. If blur=FALSE, the procedure is the same as when blur=TRUE except that a jump-preserving kernel smoothing procedure is used to obtain residuals.

Value

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

References

Qiu, P., and Kang, Y. "Blind Image Deblurring Using Jump Regression Analysis," Statistica Sinica, 25, 2015, 879-899.

See Also

roofDiff, roofEdge

Examples

data(peppers) # Peppers image is bundled with the package and it is a
              # standard test image in image processing literature.
#step.edges = stepEdgeLLK(image=peppers, bandwidth=9, thresh=17, plot=FALSE)
#parSel = roofEdgeParSel(image=peppers, bandwidth=5:6, thresh=
#c(5000,10000), nboot=10, edge1=step.edges, blur=FALSE) # Time Consuming

DRIP documentation built on April 7, 2022, 1:06 a.m.