segmSpannWilson
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Name
segmSpannWilson - segmentation by the Spann and Wilson method
segmSpannWilson [-m <m>] [-mean] [-median] [-eq] [-s <s>]
<inimage> <outimage>
Performs segmentation of every band in the inimage image
(separately) into the outimage image. The algorithm has
three steps:
-
Quad-tree smoothing
-
A quad-tree is created, and pixels are averaged 4 by 4
up in the quad-tree. n quad-tree levels are created,
including the bottom level, which is inband.
-
Local centriod clustering
-
The topmost quad-tree level is clustered by moving histogram
mass to the centroid (mean or median) of a local window moved
over the histogram. Applied iteratively, until stable. See
thresMlCentroid.
-
Boundary estimation
-
The classification result is copied down the quad-tree, but at
every level, all pixels close to a differently classified pixel
are reclassified to determine the border more precisely.
-
-n n
- Quad-tree levels. Default = 4.
-
-m m
- Use histogram window of size (2*m)+1.
Default = 0, implying search for stable value.
-
-mean
- Move histogram mass to local mean (default).
-
-median
- Move histogram mass to local median.
Use either -mean or -median (or none), not both.
If no -m option is given, a loop over the m-values
5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 is performed,
exiting if two consecutive runs give the same thresholding
result.
segmSpannWilson(3), thresMlCentroid(1), segmRandom(1)
Warning: Bad pixel type in band ..
Warning: Clustering never stable
Error : Bad m value: ..
Error : Bad number of arguments
inimage must have bands with pixel type unsigned byte.
Bands are handled one at a time, not multi-dimensionally.
-
M. Spann and R. Wilson,
- "A Quad-three Approach to Image Segmentation which
Combines Statistical and Spatial Information"
Pattern Recognition, Vol 18, Nos 3/4, pp 257-269, 1985
Tor Lønnestad, BLAB, Ifi, UiO
segmSpannWilson -median mona.img monaseg.img
segmSpannWilson -mean -n 3 mona.img monaseg.img
segmSpannWilson -m 25 -verbose mona.img monaseg.img
$Id: segmSpannWilson.c,v 1.25 1997/01/16 16:08:53 svein Exp $