[SciPy-dev] denoising spatial point process data
Fri Jan 9 11:29:43 CST 2009
2009/1/9 Sturla Molden <firstname.lastname@example.org>:
> If it is of interest I'll donate a useful algorithm of denoising spatial
> point process data to scipy.spatial. It works by fitting a mixture of two
> Poisson processes (signal + noise) using an EM algorithm. It was
> originally developed for US DoD to detect minefields using reconnaissance
> aircraft images. I use it on neurophysiological spike data to remove
> artifact waveforms. It seems to be very efficient.
It looks like a very handy procedure. As I understand it, it models
the spatial region as consisting of two regions (not necessarily
connected), each of which contains a Poisson process with a different
density, and it classifies points into one region or the other based
on the distance to their K-th nearest neighbor. As such, I wonder
whether it belongs more in with the clustering/machine learning code?
One nice aspect of having scipy.spatial is that other scipy code can
use it regardless of where that code actually is.
More information about the Scipy-dev