🚀 go-pugleaf

I'm posting this problem for a friend, and I don't have easy access
to usenet, so please reply by e-mail.  I will post a summary of the
replies.

The problem:  I have some reasonably good data, 2 pi periodic, which
indicates where, on the inside of a tube, there are holes and bends.
While it is perfectly obvious visually, I'd like to fit the data with
a fairly smooth curve that does *not* incorporate the holes, but *does*
capture most of the bends.  I have tried one approach:  a least-squares
fit of the data to the first 5 cos/sin frequencies, then weighting the
data inversely as their distance from the fit and trying again.  After
several iterations most of the outliers are not counted in the fit.  This
doesn't work so well when the pipe is bent, though, because the cross
section of a bent pipe incorporates a lot of high frequency information!

The other idea is this:  is there some way to pick a 'sufficient' number
of control points in the data automatically, without including the outliers
(such as might arise from pits in the pipe), and generate a cubic or
exponential spline that fits well with the rest of the data?  Has anyone
come across some combination of splines + least-squared fitting before?

I would appreciate references to articles, textbooks, et al.

L.G. 'Ted' Stern
lstern@orville.nas.nasa.gov