Abstract
Many fields of research in biology, motion science and robotics depend on the understanding of animal locomotion. Therefore, numerous experiments are performed using high-speed biplanar x-ray acquisition systems which record sequences of walking animals. Until now, the evaluation of these sequences is a very time-consuming task, as human experts have to manually annotate anatomical landmarks in the images. Therefore, an automation of this task at a minimum level of user interaction is worthwhile. However, many difficulties in the data—such as x-ray occlusions or anatomical ambiguities—drastically complicate this problem and require the use of global models. Active Appearance Models (AAMs) are known to be capable of dealing with occlusions, but have problems with ambiguities. We therefore analyze the application of multi-view AAMs in the scenario stated above and show that they can effectively handle uncertainties which can not be dealt with using single-view models. Furthermore, preliminary studies on the tracking performance of human experts indicate that the errors of multi-view AAMs are in the same order of magnitude as in the case of manual tracking.
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Haase, D., Nyakatura, J.A., Denzler, J. (2011). Multi-view Active Appearance Models for the X-Ray Based Analysis of Avian Bipedal Locomotion. In: Mester, R., Felsberg, M. (eds) Pattern Recognition. DAGM 2011. Lecture Notes in Computer Science, vol 6835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23123-0_2
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DOI: https://doi.org/10.1007/978-3-642-23123-0_2
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