Deformable Spatio-Temporal Shape Models: Extending ASM to 2D+Time

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 ST-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 ST-shapes in a variety of synthetic noisy data. Preliminary results on real data are also reported.

• Type: Conference
• Year: 2001
• Where: BMVC
• Authors: Ghassan Hamarneh, Tomas Gustavsson
• Keywords: Active Shape Models, BMVC 2001, Computer Vision, Optimal Proposed Deformation, Synthetic Noisy Data