Keeping Flexible Active Contours on Track Using Metropolis Updates
Abstract
Condensation, a form of likelihood-weighted particle filtering, has been successfully used to infer the shapes of highly constrained "active" con(cid:173) tours in video sequences. However, when the contours are highly flexible (e.g. for tracking fingers of a hand), a computationally burdensome num(cid:173) ber of particles is needed to successfully approximate the contour distri(cid:173) bution. We show how the Metropolis algorithm can be used to update a particle set representing a distribution over contours at each frame in a video sequence. We compare this method to condensation using a video sequence that requires highly flexible contours, and show that the new algorithm performs dramatically better that the condensation algorithm. We discuss the incorporation of this method into the "active contour" framework where a shape-subspace is used constrain shape variation.
Cite
Text
Kristjansson and Frey. "Keeping Flexible Active Contours on Track Using Metropolis Updates." Neural Information Processing Systems, 2000.Markdown
[Kristjansson and Frey. "Keeping Flexible Active Contours on Track Using Metropolis Updates." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/kristjansson2000neurips-keeping/)BibTeX
@inproceedings{kristjansson2000neurips-keeping,
title = {{Keeping Flexible Active Contours on Track Using Metropolis Updates}},
author = {Kristjansson, Trausti T. and Frey, Brendan J.},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {859-865},
url = {https://mlanthology.org/neurips/2000/kristjansson2000neurips-keeping/}
}