[Insight-developers] multi-label point sets

[M Stauffer -V-]

>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.  

OK just to make sure I understand, you did one of the options I
suggested below, i.e. to have the metric pre-process a multi-label point
set into multiple single-label point sets, and evaluate each set
individual within the metric and combine for the final metric output?

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

The idea is to put the label-specific analysis within the point-set to
simplify the metrics. That is, the code to analyze on a per-label basis
would be in the point set itself. e.g. GetNearestNeighbor would use the
point's data as a label and only consider the nearest point with the
same label. Probabilistic distance evaluation could be done in the point
set then instead of the metric. Thus the euclidean distance would be a
special case where K = 1, and the expectation-based metric could be the
same as the euclidean distance metric. Of course come to think of it, we
could merge the expectation and euclid metrics anyway, and internally
just call a different method on the point locators depending on K.

In the interest of getting reasonable working code together faster,
it'll be easier for me to add the multi-label handling to the metrics,
with a shared method for pre-processing a point set into one or more
single-label sets. Otherwise

-M


>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
>> 
>