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Markus Bullmann
2018-03-13 15:58:41 +01:00
parent 9f098887db
commit 7c407f950e
7 changed files with 42 additions and 42 deletions
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tex/chapters/usage.tex
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@@ -9,7 +9,7 @@ Consider a set of two-dimensional samples with associated weights, \eg{} generat
The overall process for bivariate data is described in Algorithm~\ref{alg:boxKDE}.
Assuming that the given $N$ samples are stored in a sequential list, the first step is to create a grid representation.
In order to efficiently construct the grid and to allocate the required memory, the extrema of the samples need to be known in advance.
In order to efficiently construct the grid and to allocate the required memory, the extrema of the samples in each dimension need to be known in advance.
These limits might be given by the application.
For example, the position of a pedestrian within a building is limited by the physical dimensions of the building.
Such knowledge should be integrated into the system to avoid a linear search over the sample set, naturally reducing the computation time.
@@ -74,4 +74,5 @@ Depending on the required accuracy, the extended box filter algorithm can furthe
Due to its simple indexing scheme, the recursive box filter can easily be computed in parallel using SIMD operations and parallel computation cores.
Finally, the most likely state can be obtained from the filtered data, \ie{} from the estimated discrete density, by searching filtered data for its maximum value.
This last step can be integrated into the last filter operation, by recording the largest output value.