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The results provided by the presented algorithm in [38] show that it closely tracks the intentional camera movements but at the cost of slightly reduced stabilization capabilities.
But now we test the hypothesis that the wind speed as random variable has the optimized CDF obtained by the presented algorithm.
In order to document the effect of the reduction of the necessary scanned path, we compare the path length saved by the presented algorithm to the standard matrix-based approach for the surface shown in Figure 3.
It is shown that under some mild conditions, the sequence generated by the presented algorithm converges strongly to the common solution of mixed equilibrium problems and fixed points problems.
It is shown that under some mild conditions the sequence { x n } generated by the presented algorithm converges strongly to the common solution of mixed equilibrium problems and fixed points problems.
The results provided by the presented algorithm in [27] show that it leads to smooth camera movement trajectory but at expense of a relative large tracking delay when the camera has intentional accelerating movement.
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Then, Section 3.4 demonstrates the presented algorithm by a worked example.
Finally, we demonstrate the effectiveness of the presented algorithm by applying it to several well-known datasets.
The results of the proposed DVS algorithm are compared with results provided by the presented algorithms in [27, 38], as the most relevant anchor algorithms.
Moreover, it is often not possible to include in an article all the necessary resources, such as data, which were processed by the presented algorithms.
The iterative methods are followed by convergence theorems and the presented algorithms are tested by solving some numerical examples.
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