DMCA
Dynamical statistical shape priors for level set based tracking (2006)
| Venue: | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Citations: | 102 - 8 self |
BibTeX
@ARTICLE{Cremers06dynamicalstatistical,
author = {Daniel Cremers and Gareth Funka-lea},
title = {Dynamical statistical shape priors for level set based tracking},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2006},
volume = {28},
pages = {1262--1273}
}
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Abstract
Abstract. In recent years, researchers have proposed to introduce statistical shape knowledge into the level set method in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of images or image sequences, so far the focus has been on statistical shape priors that are time-invariant. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes 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 into a segmentation process in a Bayesian framework for image sequence segmentation. Experiments demonstrate that such shape priors with memory can drastically improve the segmentation of image sequences. 1 Level Set Based Image Segmentation In 1988, Osher and Sethian [16] introduced the level set method 1 as a means to implicitly propagate boundaries C(t) in the image plane Ω ⊂ R 2 by evolving an
Keyphrases
dynamical statistical shape prior image sequence image segmentation statistical shape knowledge recent year image plane level set shape prior deformable object certain silhouette statistical shape prior segmentation process dynamical statistical model insufficient low-level information image sequence segmentation bayesian framework
