edci (version 1.1-3)

edgecluster: Edge detection in noisy images

Description

edgecluster is a simple combination of edgepoints and oregMclust. It just passes the results of edgepoints to oregMclust.

Usage

edgecluster(data, h1n, h2n, maxval,
    bw = max(h1n, h2n)/qnorm(0.975),
    asteps = 4, estimator = "M_median",
    kernel = "gauss", score = "gauss",
    sigma = 1, kernelfunc = NULL)

Arguments

data

See description of edgepoints.

h1n, h2n

See description of edgepoints.

asteps

See description of edgepoints.

estimator

See description of edgepoints.

kernel

See description of edgepoints.

score

See description of edgepoints.

sigma

See description of edgepoints.

kernelfunc

See description of edgepoints.

maxval

See description of eplist.

bw

See description of oregMclust.

Value

A list of two numerical matrices. The first matrix contains the results of oregMclust, which are the 'edgeclusters'. The second matrix contains the result of edgepoints.

See Also

edgepoints, oregMclust, eplist

Examples

# NOT RUN {
  # generate a 60x60 zero matrix
  y = matrix(rep(0, 60 * 60), nrow = 60)
  # set a square-shaped set of elements to 1
  y[21:40, 21:40] = 1
  # add some noise
  y = y + matrix(rnorm(60 * 60, 0, 0.2), nrow = 60)
  # plot it
  image(y, col = gray(seq(0, 1, 1/255)))

  # find edge points of the square-shaped object 
  ec = edgecluster(y, 0.05, 0.05, 0.7,
    estimator = "M_median", kernel = "gauss")
  plot(bestMclust(ec[[1]], 4), ec[[2]], xlim = c(0, 1), ylim = c(0, 1))
# }