features

Main features

High level: Statismo provides a high level interface to Statistical Models. Statismo lets you work with shapes and images instead of with Eigenvectors and Eigenvalues
Fully probabilistic: Statismo uses Probabilistic Principal Component Analysis as its underlying model, which gives a fully probabilistic interpretation to statistical shape models.
Library independent: Statismo can be used with any shape or image library (e.g. vtk, itk, ...), by writing a simple Representer class.
Reproducible: Statismo stores metadata along with the model data, to make model building reproducible.
Efficient and flexible storage: Statismo saves the data in HDF5 Format. This allows us to store all the data in a single file, which makes models easy to exchange. Furthermore, as HDF5 is supported by a large number of programs (including Matlab and R), the raw data is easy to access.
State of the art algorithms: Statismo includes algorithms for building models that are conditioned on further information (such as e.g. known partial shapes or even surrogate variables).

Eigenfaces for recognition
Turk, M. and Pentland, A.
Journal of cognitive neuroscience, 1991
Active shape models-their training and application
Cootes, T.F. and Taylor, C.J. and Cooper, D.H. and Graham, J. and others
Computer vision and image understanding, 1995
A morphable model for the synthesis of 3D faces
Blanz, V. and Vetter, T.
Proceedings of the 26th annual conference on Computer graphics and interactive techniques, 1999
Probabilistic Modeling and Visualization of the Flexibility in Morphable Models
Luethi, M. and Albrecht, T. and Vetter, T.
Mathematics of Surfaces XIII, 2009
Conditional variability of statistical shape models based on surrogate variables
Blanc, R. and Reyes, M. and Seiler, C. and Szekely, G.
Medical Image Computing and Computer-Assisted Intervention, 2009