roofEdgeParSel: roof/valley edge detection, parameter selection

View source: R/roofEdgeParSel.r

roofEdgeParSelR Documentation

roof/valley edge detection, parameter selection


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


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



A square matrix object of size n by n, no missing value allowed.


Positive integers to specify the number of pixels used in the local smoothing. These are the bandwidth parameters to be chosen from.


Threshold values to be chosen from.


Number of bootstrap samples.


Step edges. The function excludes step edges when detect roof/valley edges.


TRUE if the image contains blur, FALSE otherwise.


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.


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.


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

See Also

roofDiff, roofEdge


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.

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