A Unified Framework for Uncertainty Propagation in Automatic Shape Tracking

Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences [22]. Nevertheless, this approach required user initialization of the tracking process. This paper solves the automatic initialization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework. We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment, fusion with the predicted shape prior, and fusion with subspace constraints. As a result, we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood. Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart. Reliable detection and robust...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Xiang Sean Zhou, Dorin Comaniciu, Binglong Xie, R. Cruceanu, Alok Gupta
• Keywords: Computer Vision, CVPR 2004, Multiple Shape Candidates, Posterior Shape Model, Shape Detection, Shape Priors, Shape Tracking