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2019 | OriginalPaper | Buchkapitel

One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking

verfasst von : Oliver Mothes, Joachim Denzler

Erschienen in: Computer Vision, Imaging and Computer Graphics – Theory and Applications

Verlag: Springer International Publishing

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Abstract

In motion science, biology and robotics animal movement analyses are used for the detailed understanding of the human bipedal locomotion. For this investigations an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks, for example bone ends, have to be located and annotated in each image. In this paper we show a reduction of 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. 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.

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Titel: One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking
verfasst von: Oliver Mothes
Joachim Denzler
Verlag: Springer International Publishing
Buch: Computer Vision, Imaging and Computer Graphics – Theory and Applications
Print ISBN: 978-3-030-12208-9

Electronic ISBN: 978-3-030-12209-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12209-6_5

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