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.

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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