[Insight-developers] multi-label point sets

[Nicholas Tustison]

In my implementation for multilabel point set registration, I processed
each labeled set within the metric and then just used the existing k-d
tree to do single label point location.   That would seem the easiest
or at least that's what I thought was the easiest.  

Tell me more about the labeled point set class.  What would you add
to it to help with labeled point set registration?


On Apr 12, 2012, at 2:28 PM, M Stauffer -V- wrote:

> Hi Nick,
> 
> Brian and I have been discussing the new point set metrics and how to
> use them with labels. We're thinking we'll want to have single point
> sets containing multiple labels, and the metrics will evaluate using the
> labels. Presumably we'll store the label information in the point set
> data.
> 
> One need is to be able to perform neighbor searches constrained by
> label, and probabilistic searches e.g. for the expectation-based and
> jensen point set metrics. Currently the PointsLocator is used within the
> point set metric to find N nearest neighbors, which creates a k-d tree
> representation of the point set during pre-processing. 
> 
> Do we add per-label search directly to the point sets, presumably by
> adding a new k-d tree type? Perhaps creating one that does probabilistic
> neighbor searching as well? Or do we pre-process the user-supplied point
> sets into individual point sets for each label, and then just use the
> exciting point locator methods on those?
> 
> Do we create a separate LabeledPointSet class that knows how to do these
> things, and require that the point set metrics take this type?
> 
> What do you think?
> 
> -M
>