Abstract
Differential motion estimation is based on detecting brightness changes in local image structures. Filters approximating the local gradient are applied to the image sequence for this purpose. Whereas previous approaches focus on the reduction of the systematical approximation error of filters and motion models, the method presented in this paper is based on the statistical characteristics of the data. We developed a method for adapting separable linear shift invariant filters to image sequences or whole classes of image sequences. Therefore, it is possible to optimize the filters according to the systematical errors as well as to the statistical ones. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Krajsek, K., & Mester, R. (2007). Wiener-optimized discrete filters for differential motion estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3417 LNCS, pp. 30–41). Springer Verlag. https://doi.org/10.1007/978-3-540-69866-1_3
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