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 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.<<ETX>>