The function allows to segment an image into two or three level sets.
segment(object, level=0.5, delta = 0, thresh = 3, fov = NULL, channel = 0,
hmax = 4, aws = TRUE, varmodel = NULL, ladjust = 1.25, xind = NULL,
yind = NULL, wghts = c(0.299, 0.587, 0.114, 0), scorr = TRUE,
lkern = "Triangle", plateau = NULL, homogen = TRUE,
earlystop = TRUE, demo = FALSE, select = FALSE, sext = 1.4,
connected = FALSE, graph = FALSE, max.pixel = 400, compress = TRUE)Object of class "adimpro" with
containing a greyvalued image with 3 or 4 levels corresponding to the identified segments.
and additional list elements
containing the maximal bandwidth used
the value of parameter level used
the value of parameter delta used
the value of parameter thresh used
Image object, class "adimpro", as from
read.image, read.raw, or make.image.
center of gray/color-values of the second segment, will not be used if select=TRUE. May be specified such that either
level-delta and level+delta are within the interval (0,1) or such that they are within the interval (0,65535) (2 Byte integers).
half width of gray/color-values of the second segment, nay be increased if select=TRUE. May be specified such that either
level-delta and level+delta are within the interval (0,1) or such that they are within the interval (0,65535) (2 Byte integers).
Critical value for final assignment to segment 1 or 3 , should be specified as a quantile of the standard Gaussian distribution.
size of field of view in pixel
specifies which information to use for segmentation. 0: use grey valued image obtained from color images, 1-3: use the specified color channel.
Maximum bandwidth to use in the iteration procedure.
(logical). If TRUE the propagation - separation
(PS) approach from Polzehl and Spokoiny (2006) is used.
aws=FALSE turns off the statistical penalty resulting in a
nonadaptive kernel estimate using a kernel with bandwidth hmax.
varmodel specifies how variances are to be
estimated. This can be a homogeneous variance estimate
(varmodel="None") assuming uncorrelated errors (both spatial
and between channels). Alternatives are an adaptive homogeneous or linear
(function of the mean) variance estimate that depends on estimated
correlations and on residuals from the last iteration step.
The default varmodel=NULL corresponds to
varmodel == "Linear" if img$gamma==FALSE and
varmodel == "Constant" otherwise.
adjustment factor for lambda (>=1). Default values for
lambda are selected for Gaussian distributions. Skewed or heavy
tailed distributions may require slightly larger values for lambda
to meet the propagation condition. ladjust allows to increase
lambda in such situations.
Restrict smoothing to rectangular area defined by pixel
indices xind,yind in x- and y-direction. Full range
if NULL (default).
allows to weight the information from different (up to 4) color channels. The weights are used in the statistical penalty of the PS-procedure.
(logical). Specifies whether spatial correlation is to be
estimated. Defaults to TRUE. Is set to FALSE if
mask is not NULL.
Specifies the location kernel. Defaults to "Triangle", other choices are "Quadratic", "Cubic" and "Uniform". The use of "Triangle" corresponds to the Epanechnicov kernel nonparametric kernel regression.
Extension of the plateau in the statistical kernel. Can take
values from (0,1), defaults to 0.25.
If TRUE the algorithm determines, in each design point i, a circle of maximum radius,
such that the statistical penalty s_{ij} for all points j within the
circle is less than the value specified in plateau. In subsequent
iteration steps the statistical penalty for such points is set to zero.
This is only used if plateau>0. This results in more stable intermediate estimates and in a smoother reconstruction. homogen=TRUE
leads to increased memory requirements.
If TRUE the algorithm determines, in each design point i, a circle of minimal radius,
such that the circle includes all point j with positive weights w_{ij}.
if this radius is considerably smaller than the actual bandwidth then the
estimate in point i is fixed. This should considarably reduce computing time
in case of large hmax.earlystop=TRUE
slightly increases memory requirements.
(logical). If demo=TRUE the function pauses after each
iteration. Defaults to FALSE.
if TRUE a homogeneous rectangular region can be specified interactively. A value of level is the generated
as the median of values within the selected region.
if select==TRUE the value of delta is increased by
sext times the standard deviation (estimated by IQR) of the values in the selected region.
if TRUE the set of pixel within the same segment connected to the specified pixel is extracted.
(logical). If graph=TRUE intermediate results are
illustrated after each iteration step. Defaults to FALSE.
Maximum dimension of images for display
if graph=TRUE. If the true dimension is larger, the
images are downscaled for display. See also show.image.
logical, determines if image data are stored in raw-format.
Karsten Tabelow tabelow@wias-berlin.de and Joerg Polzehl polzehl@wias-berlin.de
The image is segmented into three parts by performing multiscale tests
of the hypotheses H1
value >= level - delta and H2 value <= level + delta.
Pixel where the first hypotesis is rejected are classified as -1 (segment 1)
while rejection of H2 results in classification 1 (segment 3).
Pixel where neither H1 or H2 are rejected ar assigned to a value 0 (segment 2). Critical values for the tests are adjusted for smoothness at the different scales inspected in the iteration process using results from multiscale testing,
see e.g. Duembgen and Spokoiny (2001). Critical values also depend on the
size of the region of interest specified in parameter fov.
Within segment 2 structural adaptive smoothing is performed while if a pair of pixel belongs to segment 1 or segment 3 the corresponding weight will be nonadaptive.
If connected==TRUE pixel in segment 2 0 are reassigned to a value 2 if they belong to a maximal connected subset of segment2 that contains the center of the specified homogeneous set.
Duembgen, L. and Spokoiny, V. (2001). Multiscale testing of qualitative hypoteses. Ann. Stat. 29, 124--152.
Polzehl, J. and Spokoiny, V. (2006). Propagation-Separation Approach for Local Likelihood Estimation. Probability Theory and Related Fields. 3 (135) 335 - 362.
read.image, read.raw, make.image, show.image, clip.image