Abstract
This contribution presents an efficient method to estimate the motion parameters of planar rigid objects in space. The problem is formulated as a model-based estimation problem which is solved by an iterative minimization strategy based on ordinary grey-scale images. Therefore no a priori knowledge as corresponding points or the displacement vector fields are required.
The advantages of the algorithm — a so called modified Newton-Raphson algorithm and its combination with Quasi-Newton methods — are the large region of stability, the high, image- bandwidth-adaptive convergence rate and the low numerical expense within each iteration step. Therefore the algorithm may be used for near realtime applications.
Furthermore the algorithm is relatively insensitive to additive noise because whole image areas are used. The variance of the motion parameters can be reduced further if the problem of motion estimation is formulated as maximum-likelihood estimation. Under a few assumptions the maximum-likelihood estimation can be interpreted as a generalized correlation, i.e. the correlation of the appropriately prefiltered images.
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Diehl, N., Burkhardt, H. (1989). Motion Estimation in Image Sequences. In: Linkwitz, K., Hangleiter, U. (eds) High Precision Navigation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-74585-0_22
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DOI: https://doi.org/10.1007/978-3-642-74585-0_22
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