Abstract
Differential motion estimation in image sequences is based on measuring the orientation of local structures in spatio-temporal signal volumes. For this purpose, discrete filters which yield estimates of the local gradient are applied to the image sequence. Whereas previous approaches to filter optimization concentrate on the reduction of the systematical error of filters and motion models, the method presented in this paper is based on the statistical characteristics of the data. We present a method for adapting linear shift invariant filters to image sequences or whole classes of image sequences. We show how to simultaneously optimize derivative filters according to the systematical errors as well as to the statistical ones.
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Krajsek, K., Mester, R. (2005). Signal and Noise Adapted Filters for Differential Motion Estimation. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_59
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DOI: https://doi.org/10.1007/11550518_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
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