Dynamical Statistical Shape Priors for Level Set-Based Tracking

In recent years, researchers have proposed to introduce statistical shape knowledge into level set based segmentation methods in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of familiar objects, so far the focus has been on statistical shape priors which are static in time. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes (such as those of a walking person) may become more or less likely over time. In this paper, we tackle the challenge of learning dynamical statistical models for implicitly represented shapes. We show how these can be integrated as dynamical shape priors in a Bayesian framework for level set based image sequence segmentation. We assess the effect of such shape priors "with memory" on the tracking of familiar deformable objects in the presence of noise and occlusion. We show comparisons between dynamical and static shape priors, between mode...

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Daniel Cremers
• Keywords: PAMI 2006, Shape Priors, Statistical Shape, Statistical Shape Priors