roofEdgeParSel {DRIP} | R Documentation |
Parameter Selection in Roof Edge Detection
Description
Select bandwidth and threshold value for the roof/valley edge detector using bootstrap.
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 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 values with each entry corresponding to each combination
of bandwdith and threshold.
References
Qiu, P. and Kang, Y. (2015) “Blind Image Deblurring Using Jump Regression Analysis”, Statistica Sinica, 25, 879-899, doi:10.5705/ss.2014.054
See Also
Examples
## 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)