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Then, the overhead becomes a 0.5 point search.
Watershed segmentation; Saddle point search; Noise removal; Substructure merging.
The extremum point search is one of the two key steps for finding the minimax approximation.
See Algorithm 1 for the pseudocode describing the saddle point search on each task.
The optimum perturbation signal is found with a closest point search in a lattice.
The closeness constraint implements a local closest point search from the second image to the first image.
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c Two-point search.
Hence, the overhead for the proposed algorithm corresponds to a two-point search in total.
Figure 1c shows all possible cases of a two-point search applied for a PU.
The next step tests the necessity of two-point search based on the observation that a two-point search should be performed if this distance for any PU is equal to one.
Hence, in the following, we solve items 3 5 in Algorithm 1 using fixed-point search routines.
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