This paper extends 2D Active Shape Models to 2D+Time by presenting a method for modelling and segmenting spatio-temporal shapes (ST-shapes). The modelling part consists of constructing a statistical model of ST-shape parameters. The model obtained describes the principal modes of variation of the ST-shape in addition to certain constraints on the allowed variations. An active approach is used in segmentation; an initial spatio-temporal shape is deformed to better fit the data and the optimal proposed deformation is calculated using dynamic programming. The results presented show the proposed method detecting spatio-temporal shapes in a variety of synthetic noisy data. Preliminary results on real data are also reported.
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