Description Usage Arguments Details Value Author(s) References See Also Examples
Detection of edge points by the difference of two rotated and asymmetric Kernel or MKernelEstimators.
1 2 3 4 
data 
numerical matrix representation of the (noisy) image. 
h1n, h2n 
positive numbers. Bandwidth for the kernels. 
asteps 
optional positive integer. Number of different angles used. 
estimator 
optional string. Estimator used within the windows. Possible values are:

kernel 
optional string. Kernel function for

score 
optional string. Score function for MKernelEstimators
if

sigma 
optional positiv number. Parameter for the score function

kernelfunc 
optional function taking two numbers as arguments
and returning a positive number. Used as kernel function given

margin 
Optional value. Results near the margin are in
general not very reasonable. Setting 
edgepoints
implements several versions of the RDKE method,
introduced by Qiu in 1997.
The original method, which uses kernel estimates, is a generalized
version which uses MKernelEstimators and two test procedures. The
test procedures are multiple tests for different angles for the
hypothesis of equal means (or medians) in both windows.
All methods apply rotating and scaling in the correct order (see
Garlipp, 2004).
A list of two numerical matrices. The first matrix contains the maximal jump height for every pixel if the chosen estimator is not a test procedure, and pvalues otherwise. The second matrix contains the angle which leads to the maximal jump height or minimal pvalue.
Tim Garlipp, [email protected]
Garlipp, T. (2004), On Robust Jump Detection in Regression Surface with Applications to Image Analysis, CarlvonOssietzkyUniversitaet Oldenburg, Dissertation
Qiu, P. (1997), Nonparametric Estimation of Jump Surface, The Indian Journal of Statistics, 59A, No.2, 268294.
1 2 3 4 5 6 7 8 9 10  ## produce a matrix representation of a simple
## noisy image showing a black rectangle
y = matrix(rep(0, 60 * 60), nrow = 60)
y[21:40, 21:40] = 1
y = y + matrix(rnorm(60 * 60, 0, 0.2), nrow = 60)
image(y, col = gray(seq(0, 1, 1/255)))
## find the rectangle's edge points
ye = edgepoints(y, 0.05, 0.05, estimator = "M_median", kernel = "gauss")
image(ye[[1]] > 0.7, col = gray(c(1,0)))

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