Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An adaptive regularized recursive displacement estimation algorithm is presented. An estimate of the displacement vector field (DVF) is obtained by minimizing the linearized displaced frame difference (DFD) using nu subsets (submasks) of a set of points that belong to a causal neighborhood (mask) around the working point. Assuming that the displacement vector is constant at all points inside the mask, nu systems of equations are formed based on the corresponding submasks. A set theoretic regularization approach is follod for solving this system of equations by using information about the noise and the solution. An expression for the variance of the linearization error is derived in quantifying the information about the noise. Prior information about the solution is incorporated into the algorithm using a causal oriented smoothness constraint (OSC) which also provides a spatially adaptive prediction model for the estimation DVF. It is shown that certain existing regularized recursive algorithms are special cases of the proposed algorithm, if a single mask is considered. Based on experiments with typical videoconferencing scenes, the improved performance of the proposed algorithm with respect to accuracy, robustness to occlusion and smoothness of the estimated DVF is demonstrated. >
['Serafim N. Efstratiadis', 'Aggelos K. Katsaggelos']
I am writing a research paper on a idea An adaptive regularized recursive displacement estimation algorithm
[{"from": "human", "value": "I am writing a research paper on a idea An adaptive regularized recursive displacement estimation algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An adaptive regularized recursive displacement estimation algorithm is presented. An estimate of the displacement vector field (DVF) is obtained by minimizing the linearized displaced frame difference (DFD) using nu subsets (submasks) of a set of points that belong to a causal neighborhood (mask) around the working point. Assuming that the displacement vector is constant at all points inside the mask, nu systems of equations are formed based on the corresponding submasks. A set theoretic regularization approach is follod for solving this system of equations by using information about the noise and the solution. An expression for the variance of the linearization error is derived in quantifying the information about the noise. Prior information about the solution is incorporated into the algorithm using a causal oriented smoothness constraint (OSC) which also provides a spatially adaptive prediction model for the estimation DVF. It is shown that certain existing regularized recursive algorithms are special cases of the proposed algorithm, if a single mask is considered. Based on experiments with typical videoconferencing scenes, the improved performance of the proposed algorithm with respect to accuracy, robustness to occlusion and smoothness of the estimated DVF is demonstrated. >"}]