@inproceedings{amthor2014robust,
    type = {inproceedings},
    key = {amthor2014robust},
    title = {Robust Pictorial Structures for X-ray Animal Skeleton Tracking},
    author = {Manuel Amthor and Daniel Haase and Joachim Denzler},
    booktitle = {International Conference on Computer Vision Theory and Applications (VISAPP)},
    year = {2014},
    pages = {351-359},
    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. },
    groups = {locomotion},
}