AFNI program: 3dedge3
Output of -help
Usage: 3dedge3 [options] dset dset ...
Does 3D Edge detection using the library 3DEdge by;
by Gregoire Malandain (gregoire.malandain@sophia.inria.fr)
Options :
-input iii = Input dataset
-verbose = Print out some information along the way.
-prefix ppp = Sets the prefix of the output dataset.
-datum ddd = Sets the datum of the output dataset.
-fscale = Force scaling of the output to the maximum integer range.
-gscale = Same as '-fscale', but also forces each output sub-brick to
to get the same scaling factor.
-nscale = Don't do any scaling on output to byte or short datasets.
-scale_floats VAL = Multiply input by VAL, but only if the input datum is
float. This is needed when the input dataset
has a small range, like 0 to 2.0 for instance.
With such a range, very few edges are detected due to
what I suspect to be truncation problems.
Multiplying such a dataset by 10000 fixes the problem
and the scaling is undone at the output.
-automask = For automatic, internal calculation of a mask in the usual
AFNI way. Again, this mask is only applied after all calcs
(so using this does not speed up the calc or affect
distance values).
** Special note: you can also write '-automask+X', where
X is some integer; this will dilate the initial automask
number of times (as in 3dAllineate); must have X>0.
References for the algorithms:
- Optimal edge detection using recursive filtering
R. Deriche, International Journal of Computer Vision,
pp 167-187, 1987.
- Recursive filtering and edge tracking: two primary tools
for 3-D edge detection, O. Monga, R. Deriche, G. Malandain
and J.-P. Cocquerez, Image and Vision Computing 4:9,
pp 203-214, August 1991.
++ Compile date = Dec 17 2024 {AFNI_24.3.10:linux_ubuntu_24_64}
This page auto-generated on
Tue Dec 17 06:52:37 PM EST 2024