connected
Connected components of an image or window
Finds the topologically-connected clumps of pixels in an image or window.
Usage
connected(X, background = NA, method="C")Arguments
- X
- Image (object of class
"im") or window (object of class"owin"). - background
- Optional. Treat pixels with this value as being part of the background.
- method
- String indicating the algorithm to be used. Either
"C"or"interpreted". See Details.
Details
This function computes 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 (in the 8-connected
sense). 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.
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 Rimplementation of the algorithm of Park et al (2000).
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.
Value
- A pixel image (object of class
"im") with factor values. The levels of the factor correspond to the connected components.
Warnings
It may be hard to distinguish different components
in the default plot because the colours of nearby components may be
very similar. See the Examples for a randomised colour map.
The algorithm for method="interpreted"
can be very slow for large images (or images where
the connected components include a large number of pixels).
References
Park, J.-M., Looney, C.G. and Chen, H.-C. (2000) Fast connected component labeling algorithm using a divide and conquer technique. Pages 373-376 in S.Y. Shin (ed) Computers and Their Applications: Proceedings of the ISCA 15th International Conference on Computers and Their Applications, March 29-31, 2000, New Orleans, Louisiana USA. ISCA 2000, ISBN 1-880843-32-3.
Rosenfeld, A. and Pfalz, J.L. (1966) Sequential operations in digital processing. Journal of the Association for Computing Machinery 13 471-494.
See Also
Examples
data(cells)
d <- distmap(cells, dimyx=256)
X <- levelset(d, 0.06)
plot(X)
Z <- connected(X)
plot(Z)
# number of components
nc <- length(levels(Z))
# plot with randomised colour map
plot(Z, col=hsv(h=sample(seq(0,1,length=nc), nc)))
# how to extract the components as a list of windows
W <- tiles(tess(image=Z))