edgepoints {edci} | R Documentation |
Detection of edgepoints by the difference of two rotated and asymmetric Kernel- or M-Kernel-Estimators.
edgepoints(data, h1n, h2n, asteps = 4, estimator = "kernel", kernel = "mean", score = "gauss", sigma = 1, kernelfunc = NULL, margin = FALSE)
data |
numerical matrix describing 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 if
estimator="kernel" , estimator="M_mean" , or
estimator="M_median" . Possible values are:
|
score |
optional string. Score function for M-Kernel-Estimators
if estimator="M_mean" or
estimator="M_median" . Possible values are:
|
sigma |
optional positiv number. Parameter for the score function
"gauss" or "huber" . |
kernelfunc |
optional function taking two numbers as arguments
and returning a positive number. Used as kernelfunction if
kernel="func" is given. Note that the function should be zero
outside [-1,1]x[-1,1] and that only one function is given for both
windows. The 'lower' part of the domain, e.g. [-1,1]x[-1,0], is used
within one window while the 'upper' part is used within the other. |
margin |
Optional value. Results near the margin are in
general not very reasonable. Setting margin=TRUE they are
calculated nevertheless. With margin=FALSE the returned
matrices have the same dimension as data but the jump heights at
the margin are set to zero. Setting margin="cut" the retuned
matrices are cutted down by the margins. The default is
margin=FALSE . |
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 M-Kernel-Estimators, and two test procedures. The
test procedures are multiple tests for different angles for the
hypothesis, that there are equal means (or medians) in both windows.
All methods apply rotating and scaling in the correct order (see
Garlipp, 2004).
Return value is a list of two numerical matrices. The first matrix contains the maximal jump height for every pixel, if the choosen estimator is not a test procedure, and p-values otherwise. The second matrix contains the angle, which leads to the maximal jump heigth or minimal p-value.
Tim Garlipp, garlipp@mathematik.uni-oldenburg.de
Garlipp, T. (2004), On Robust Jump Detection in Regression Surface with Applications to Image Analysis, Carl-von-Ossietzky-Universität Oldenburg, Dissertation
Qiu, P. (1997), Nonparametric Estimation of Jump Surface, The Indian Journal of Statistics, 59A, No.2, 268-294.
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))) ye <- edgepoints(y,0.05,0.05,estimator="M_median",kernel="gauss") image(ye[[1]]>0.7,col=gray(c(1,0)))