Adaptive multiple-input constrained pel-recursive displacement estimation

An adaptive multiple-input pel-recursive algorithm is presented. The displacement vector field (DVF) is estimated by minimizing the linearized displaced frame difference (DFD) using nu submasks of causal mask around the working point. Then, nu corresponding systems of equations are formed and the se...

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Bibliographic Details
Main Authors: Efstratiadis, S.N., Katsaggelos, A.K.
Format: Conference Proceeding
Language:English
Subjects:
Constraint theory
Design for disassembly
Equations
Laboratories
Motion estimation
Noise robustness
Optical filters
Predictive models
Stochastic processes
Tracking
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Summary:An adaptive multiple-input pel-recursive algorithm is presented. The displacement vector field (DVF) is estimated by minimizing the linearized displaced frame difference (DFD) using nu submasks of causal mask around the working point. Then, nu corresponding systems of equations are formed and the set theoretic regularization approach results in a weighted constrained least-squares estimation of the DVF by using information about the variance of the linearization error (noise) and the solution. The prior information about the solution is incorporated into the algorithm using a causal oriented smoothness constraint (OSC), which also provides a spatial prediction model for the estimated DVF. The improved performance of the proposed algorithm with respect to accuracy, robustness to occlusion, and smoothness of the estimated DVF is demonstrated.< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1992.226205