[SciPy-user] Finding clustered vectors
david.huard at gmail.com
Tue Nov 21 21:17:04 CST 2006
You could try using functions from scipy.cluster. The kmeans algorithm may
be what you are looking for. However, I'm not sure how well this will work
if you have very small clusters (two values is not much to work with). You
could also build an empirical cdf of the shifts, and find the region where
the cdf makes a abrupt step.
2006/11/21, Aaron Hoover <ahoover at eecs.berkeley.edu>:
> Hi all,
> I've got a question maybe some of you can help me with. I have two
> images, one of which is a planar translation of the other, so I know
> it contains some of the same "features" (using this loosely) as the
> first image. I have some different approaches matching features and
> using those results to calculate candidate 2D shift vectors for each
> 2 image sequence. Most of the time, those vectors aren't all the same
> due to noise, imperfect matching, features leaving the frame, etc.
> However there are usually at least a few whose dx and dy values are
> very close, while the others are somewhat noisy and relatively
> randomly distributed.
> My question is, what's the best way to get those values in that
> cluster? I've tried some very simple statistical methods, but because
> of outliers and things, simply comparing to the mean and/or variance
> hasn't worked too well. Basically, if I *know* there's going to be a
> cluster of at least 2 vectors with nearly the same values how can I
> easily extract those values (or the mean of those values)?
> SciPy-user mailing list
> SciPy-user at scipy.org
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