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The likelihood evaluation relying on occupancy and color information is computed on local neighborhoods, thus dramatically decreasing the computation load of the overall algorithm.
These points will not be computed in the subsequent iterations, and thus the computation load is reduced, especially for the long contours.
However, the computation load cannot be neglected.
Still, the overall computation load can be quite large.
That way the computation load can be kept relatively small.
We recall that the three algorithms, MGS, RMGS, and OOMP are equivalent except on computation load.
However, the full search algorithm notoriously needs high computation load and large memory size [12 14].
The computation load becomes heavy when the size of is large.
To reduce computation load, a reduced-dimension MUSIC method was proposed in [10].
Firstly, it avoids exhaustive search and greatly reduces the computation load [17].
High-order cumulant method requires the signal statistical properties, and it needs a heavy computation load.
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