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Due to the applied error boundary, the system works accurately, however the query tree has to be iteratively remade in each mean shift iteration at the cost of additional computational overhead.
Three factors affect the observed speedup of the mean shift iteration, these are: the size of the image, the kernel bandwidth and finally the number of kernels.
The running time of a parallel mean shift iteration was measured on the different devices in order to observe the scaling of the data parallel scheme.
The mean shift iteration is then started from these seed points, and the other elements of the feature space are assigned to the so-obtained modes by using certain local rules [12, 15, 17, 18, 27, 29].
As a result of the different parametrizations, the mean shift iteration was timed in 60 different constellations on the 5 GPGPU devices (plus the CPU) with each measurement indicating an average value recorded on 100 iterations (see Sect. 4.6).
DeMenthon et al. [6] reached lower complexity by applying an increasing bandwidth for each mean shift iteration.
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Impact on the number of mean shift iterations.
where l ∈ [0, k + 1] denotes the mean shift iterations.
Finally, the next mean shift iterations of GMM-SAMT can be initialiezed.
The following aspects were analyzed: 1. Impact on the number of mean shift iterations.
Thus, the object's changes in position and scale can be evaluated through the mean shift iterations simultaneously.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com