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However, we try to move our algorithm from supervised texture classification to unsupervised terrain perception.
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We have presented our work in progress to move our extremely fast quartet phylogeny algorithm from being a theoretical result to a practical algorithm for inferring trees.
As soon as no such move exists, our algorithm terminates.
Once all pairwise similarities have been calculated, our algorithm moves on to the first of two clustering processes (see Figure 1).
Therefore, we render a large number of cubes that have random positions and random moving speeds to evaluate our algorithm in terms of performance and graphics memory.
To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)—vehicle counting.
In this section, we describe our distributed prevent and move away (PMA) algorithm, which is fully distributed and does not require excessive information exchange.
In [34] we evaluated our algorithm to detect moving sub-groups of firefighters during the described training scenario by comparing the results to a manually annotated ground truth.
In Figure 10, we show the results of our algorithm as it is moving from rugged terrain to a smoother terrain from different angles.
Other sites are trying to move past the algorithm.
We need to move beyond the algorithm bravado to engage more fundamentally with the craft.
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