The function connected is generic, with methods
for pixel images (class "im") and windows (class "owin")
described here. There are also methods for
tessellations (connected.tess),
point patterns (connected.ppp and
connected.lpp),
and linear networks (connected.linnet).
The functions described here compute the connected component transform
(Rosenfeld and Pfalz, 1966)
of a binary image or binary mask. The argument X is first
converted into a pixel image with logical values. Then the algorithm
identifies the connected components (topologically-connected clumps
of pixels) in the foreground.
Two pixels belong to the same connected component if they have the value
TRUE and if they are neighbours.
This rule is applied repeatedly until it terminates.
Then each connected component
contains all the pixels that can be reached by stepping from neighbour
to neighbour.
Pixels are defined to be neighbours if they are physically adjacent to
each other. If connect=4, each pixel has 4 neighbours,
lying one step above or below, or one step to the left or right.
If connect=8 (the default), each pixel has 8 neighbours,
lying one step above or below, or one step to the left or right,
or one diagonal step away. (Pixels at the edge of the image have fewer
neighbours.) The 8-connected algorithm is the default
because it gives better results when the pixel grid is coarse. The
4-connected algorithm is faster and is recommended when the pixel grid
is fine.
If method="C", the computation is performed by a compiled C language
implementation of the classical algorithm of Rosenfeld and Pfalz
(1966). If method="interpreted", the computation is performed
by an R implementation of the algorithm of Park et al (2000).
By default, the result is a factor-valued image,
with levels that correspond to
the connected components. The Examples show how to extract each
connected component as a separate window object.
If X is a window and polygonal=TRUE,
the result is a tessellation
(object of class "tess") whose tiles are the connected components.