smp.smooth
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| smp.smooth [2010/10/27 16:24] – created jochen | smp.smooth [2010/10/28 00:09] (current) – updated the algorithm description to reflect an update in code jochen | ||
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| ===== Requirements ===== | ===== Requirements ===== | ||
| - | To perform the smoothing, you must have a valid SMP object as well as the SRF object that contains the respective, neighborhood information. | + | To perform the smoothing, you must have a valid SMP object as well as the SRF object that contains the respective neighborhood information. |
| ===== Notes ===== | ===== Notes ===== | ||
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| After creating the 2D kernel weights, the smoothing is applied in iterations. During each iteration, each vertices value is replaced by a weighted sum of the value itself and an equally inter-neighbor weighted sum of the first-degree neighbors: | After creating the 2D kernel weights, the smoothing is applied in iterations. During each iteration, each vertices value is replaced by a weighted sum of the value itself and an equally inter-neighbor weighted sum of the first-degree neighbors: | ||
| - | {{: | + | {{: |
| In other words, the central kernel weight remains with the old value and the remainder to 1 is split across the next neighbors according to the kernel weight given by their distance to the vertex. This approach takes both the neighborhood and the distance of vertices into account. | In other words, the central kernel weight remains with the old value and the remainder to 1 is split across the next neighbors according to the kernel weight given by their distance to the vertex. This approach takes both the neighborhood and the distance of vertices into account. | ||
| + | After the smoothing is performed, the mean of the map is adjusted to reflect the original mean value (to counter rounding errors if a high number of iterations was used), **if and only if** the standard deviation is not smaller than the sum (for most skewed maps this is the case). | ||
smp.smooth.1288196688.txt.gz · Last modified: 2010/10/27 16:24 by jochen