View source: R/roofEdgeParSel.R
roofEdgeParSel | R Documentation |
Select bandwidth and threshold value for the roof/valley edge detector using bootstrap.
roofEdgeParSel(image, bandwidth, thresh, nboot, edge1, blur = FALSE)
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. |
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.
Yicheng Kang
Qiu, P. and Kang, Y. (2015) “Blind Image Deblurring Using Jump Regression Analysis”, Statistica Sinica, 25, 879-899, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5705/ss.2014.054")}
roofDiff
, roofEdge
## Not run:
step.edges <- stepEdge(peppers, bandwidth = 9, thresh = 17)
set.seed(24)
parSel <- roofEdgeParSel(image = peppers, bandwidth = 5, thresh = 5000,
nboot = 1, edge1 = step.edges, blur = TRUE) # Time Consuming
## End(Not run)
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