@article{Haase14:CLA,
    type = {article},
    key = {Haase14:CLA},
    title = {Comparative Large-Scale Evaluation of Human and Active Appearance Model Based Tracking Performance of Anatomical Landmarks in X-ray Locomotion Sequences},
    author = {Daniel Haase and John A. Nyakatura and Joachim Denzler},
    journal = {Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA)},
    year = {2014},
    number = {1},
    pages = {86-92},
    volume = {24},
    abstract = {The detailed understanding of animal locomotion is an important part of biology, motion science and robotics. To analyze the motion, high-speed x-ray sequences of walking animals are recorded. The biological evaluation is based on anatomical key points in the images, and the goal is to find these landmarks automatically. Unfortunately, low contrast and occlusions in the images drastically complicate this task. As recently shown, Active Appearance Models (AAMs) can be successfully applied to this problem. However, obtaining reliable quantitative results is a tedious task, as the human error is unknown. In this work, we present the results of a large scale study which allows us to quantify both the tracking performance of humans as well as AAMs. Furthermore, we show that the AAM-based approach provides results which are comparable to those of human experts.},
    dateadded = {2013-11-19},
    groups = {locomotion},
    issn = {1054-6618},
    url = {http://link.springer.com/article/10.1134%2FS1054661814010222},
}