Robust Pictorial Structures for X-ray Animal Skeleton Tracking

Manuel Amthor, Daniel Haase, Joachim Denzler

2014

Abstract

The detailed understanding of animals in locomotion is a relevant field of research in biology, biomechanics and robotics. To examine the locomotor system of birds in vivo and in a surgically non-invasive manner, high-speed X-ray acquisition is the state of the art. For a biological evaluation, it is crucial to locate relevant anatomical structures of the locomotor system. There is an urgent need for automating this task, as vast amounts of data exist and a manual annotation is extremely time-consuming. We present a biologically motivated skeleton model tracking framework based on a pictorial structure approach which is extended by robust sub-template matching. This combination makes it possible to deal with severe self-occlusions and challenging ambiguities. As opposed to model-driven methods which require a substantial amount of labeled training samples, our approach is entirely data-driven and can easily handle unseen cases. Thus, it is well suited for large scale biological applications at a minimum of manual interaction. We validate the performance of our approach based on 24 real-world X-ray locomotion datasets, and achieve results which are comparable to established methods while clearly outperforming more general approaches.

Paper Citation

in Harvard Style

Amthor M., Haase D. and Denzler J. (2014). Robust Pictorial Structures for X-ray Animal Skeleton Tracking . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 351-359. DOI: 10.5220/0004693403510359

in Bibtex Style

@conference{visapp14,
author={Manuel Amthor and Daniel Haase and Joachim Denzler},
title={Robust Pictorial Structures for X-ray Animal Skeleton Tracking},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={351-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004693403510359},
isbn={978-989-758-009-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Robust Pictorial Structures for X-ray Animal Skeleton Tracking
SN - 978-989-758-009-3
AU - Amthor M.
AU - Haase D.
AU - Denzler J.
PY - 2014
SP - 351
EP - 359
DO - 10.5220/0004693403510359