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Originally Posted by JustChecking ..otherwise it puts a colour "corresponding" to the amount of the change there |
OK, when the radius is pretty small - then you can think of it as some sort of first derivative function where the result will represent the amount of change. But what gets me is why, for larger radii, the output image reverts to the original?
I think that the "descriptive" name is the best way to understand it because it relates well to a high-pass
audio filter - if you lower the cut-off frequency more of the original gets through.
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Originally Posted by JustChecking ...and the 0.1 isn't typo - it's just the minimal value you can choose |
OK, true that it
is the minimal value, but when you apply it to an image you get
absolutely nothing (just mid-grey). Using a value of 1.0 you'll get something like the result of a simple Laplace filter - all edges
and noise.
Getting back to Doug's question:
- if we have to resort to "nerd" talk;
- rely on analogies from other areas;
- the help text gives us something that just doesn't work.
then it
is pretty hard to understand.
for JustChecking: Seems that you work in this general area, so let me ask: There's a lot of "rocket science" image processing going on (Fast-fourier, C/C++ libraries etc..) - is there a way that we "mere mortals" can use some of this stuff?
Rô