Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models

Oliver Mothes, Joachim Denzler

2017

Abstract

For animal bipedal locomotion analysis, an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks have to be annotated in each image. In this paper we reduce this effort by automating the annotation with a minimum level of user interaction. Recent approaches, based on Active Appearance Models, are improved by priors based on anatomical knowledge and an online tracking method, requiring only a single labeled frame. However, the limited search space of the online tracker can lead to a template drift in case of severe self-occlusions. In contrast, we propose a one-shot learned tracking-by-detection prior which overcomes the shortcomings of template drifts without increasing the number of training data. We evaluate our approach based on a variety of real-world X-ray locomotion datasets and show that our method outperforms recent state-of-the-art concepts for the task at hand.

Paper Citation

in Harvard Style

Mothes O. and Denzler J. (2017). Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 246-254. DOI: 10.5220/0006133302460254

in Bibtex Style

@conference{visapp17,
author={Oliver Mothes and Joachim Denzler},
title={Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={246-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006133302460254},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Anatomical Landmark Tracking by One-shot Learned Priors for Augmented Active Appearance Models
SN - 978-989-758-227-1
AU - Mothes O.
AU - Denzler J.
PY - 2017
SP - 246
EP - 254
DO - 10.5220/0006133302460254