[Date Index] [Thread Index] [Author Index]

Re: Surface Smoothing

• To: mathgroup at smc.vnet.net
• Subject: [mg127638] Re: Surface Smoothing
• From: "Nicholas Kormanik" <nkormanik at gmail.com>
• Date: Thu, 9 Aug 2012 03:53:03 -0400 (EDT)
• Delivered-to: l-mathgroup@mail-archive0.wolfram.com
• Delivered-to: l-mathgroup@wolfram.com
• Delivered-to: mathgroup-newout@smc.vnet.net
• Delivered-to: mathgroup-newsend@smc.vnet.net
• References: <jvo0fu$cfq$1@smc.vnet.net> <5021312C.1030703@KevinMcCann.com>
• Reply-to: <nkormanik at gmail.com>

Good point, Kevin.

The spiky behavior does make things rather scary.

By incorporating additional factors (beyond the two in the contour map) I
hope to minimize the bad spikes, and maximize the good ones.  For now,
though, I hope to find a relatively decent "neighborhood," should such
actually exist - i.e., the "sweet spot."

An analogy might be:  A dangerous minefield.  If I absolutely have to walk
through it, I'd like to try to ascertain the path with the lowest
probability of being blown up.

Nicholas Kormanik

-----Original Message-----
From: Kevin J. McCann [mailto:kjm at KevinMcCann.com] 
Sent: Tuesday, August 07, 2012 9:16 AM
To: nkormanik at gmail.com
Subject: [mg127638] Re: Surface Smoothing

 

Any smoothing implicitly assumes that you "know" what the data should look
like. So, I assume that you know that the spiky behavior is not "correct".
Given that, how about a LSQ fit to some satisfactorily smooth function, e.g.
a 2d polynomial or a truncated Fourier series?

 

Kevin