Quote:
Originally posted by Didée
Then you catched me on notation & terminology. So this means, the diameter of the used convolution kernel represents the radius of the gaussian? Doesn't seem obvious to me, but if it is like that, well, let it be ...
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It should be more evident if you look at a larger radius. The
radius of the gaussian is not a function of the number of coefficients in the kernel (they're all infinitely wide if you calculate them to sufficient precision), but rather of the number of nontrivial coefficients. Your rx=256 would be equivalent to a kernel like
".01 .01 .01 .01 .02 .02 .03 .04 .05 .06 .07 .09 .11 .13 .15 .18 .21 .24 .28 .32 .37 .42 .47 .52 .57 .62 .68 .73 .78 .83 .87 .91 .94 .97 .98 1.00 1.00 1.00 .98 .97 .94 .91 .87 .83 .78 .73 .68 .62 .57 .52 .47 .42 .37 .32 .28 .24 .21 .18 .15 .13 .11 .09 .07 .06 .05 .04 .03 .02 .02 .01 .01 .01 .01"
I call that radius=16.
<edit> To be exact, radius = the distance from the center to the coefficient with value 0.37 = 1/
e (assuming the center coeff is 1)
</edit>