edgecluster: Edge detection in noisy images

In edci: Edge Detection and Clustering in 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.

Author(s)

Tim Garlipp, [e-mail]

See Also

edgepoints, oregMclust, eplist

Examples

  # 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))

Example output

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edci documentation built on May 1, 2019, 7:44 p.m.