[vtkusers] Statistical Model with partially available observations (using vtkPCAStatistics and vtkPCAAnalysisFilter)

[Dänzer, Stefan]

Dear List,

I'm a research assistant at the Innovation Center Computer Assisted Surgery (ICCAS) in Leipzig, Germany. I'm working on Active Shape Models and Active Appearance Models at the moment. I've discovered two classes of interest for me in vtk. These are:
vtkPCAAnalysisFilter (Intended for spatially corresponding points on shape models) and
vtkPCAStatistics (A more general filter to assess observations and project those observations in the eigenspace of their covariance matrix. This is what I use for creating statistical models of texture, e.g. Active Appearance Models)

I was wondering how these two filters cope with incomplete observations, e.g. missing data components in the observations.

Let me illustrate this by an example. If we want to build a statistical model of an object in an image, say an apple in a photograph. Assume we have several photos of apples and their respective segmentations as training data for our model. Further assume some apples are only contained partially in some of the photos. Statistically speaking, those apples represent partial observations. I was wondering how to best incorporate those partial observations in a statistical model which could be built by the two mentioned vtk filters.

Regards,

stefan

_______________________________________________________________________
Dipl. Inform. Stefan Daenzer
Research Associate | Working Group - Scientific Methods

Universität Leipzig | Faculty of Medicine
Innovation Center Computer Assisted Surgery (ICCAS)
Semmelweisstr. 14
D - 04103 Leipzig
Germany

Phone: ++49 (0) 341 / 97 - 1 20 03
Fax: ++49 (0) 341 / 97 - 1 20 09
Email: stefan.daenzer at iccas.de

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