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scott <sco### [at] scottcom> wrote:
> You can
> then do a fourier transform along x, and the frequency peak in the
> result will correspond to the number of teeth, and be far more immune to
> any artefacts/noise in the image.
I worked a little bit more on this, and plotted out the radius vs arc-length to
get a triangle wave graph (3600 samples). Naturally, I thought that the
Fourier transform would be a great way to pluck-out the frequency of the teeth,
I just haven't gotten to that point yet.
I'm glad to see that you actually do this, and know empirically that it's a
reliable and robust method. That validates spending the time to go that route.
Thanks! :)
Based on my graph, there does seem to be some "cruft" at some point on the gear
photo that distorts the data, but it's apparently still clean enough to give me
the correct number of teeth with the way I'm currently doing it, provided I
apply the correct frequency of sampling. (see binary thread)
I was also thinking that there ought to be way to determine a radial sampling
frequency based on the resolution of the image - since any higher than that
wouldn't gain me anything.
Until I work any of that out, I might just think about pre-scanning the
perimeter, estimating the tooth number and average size of a tooth, and then
filtering out any noise that disrupts my counting algorithm by ignoring any
change that's less than a certain fraction of a tooth height.
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