Abstract
We describe a Bayesian motion estimation algorithm which is part of a temporally recursive noise reduction filter for X-ray fluo-roscopy images. Our algorithm draws its robustness against high quan-tum noise levels from a statistical regularization, where a priori expecta-tions about the spatial and temporal smoothness of motion vector fields are modelled by generalized Gauss-Markov random fields. We show that by using generalized Gauss-Markov random fields both smoothness and motion edges can be captured, without the need to specify an often crit-ical edge detection threshold. Instead, our algorithm controls edges by a single parameter by means of which the regularization can be tuned from a median-filter like behaviour to a linear-filter like one.
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© 1998 Springer-Verlag Berlin Heidelberg
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Aach, T., Kunz, D. (1998). Robust Motion Vector Relaxation for X-Ray Fluoroscopy Using Generalized Gauss-Markov Random Fields. In: Lehmann, T., Metzler, V., Spitzer, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 1998. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58775-7_2
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DOI: https://doi.org/10.1007/978-3-642-58775-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63885-8
Online ISBN: 978-3-642-58775-7
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