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Moreover, AI CNN with a kernel size of 6 × 6 shows remarkable robustness under different light conditions, sizes and textures of aggregates.
The best size of the kernels is 6 × 6, and an AI CNN with a kernel size of 6 × 6 has a 0.0938 relative error for evaluating the AI using 300 PPI images.
The upper limit is the error of the MD method, and the lower limit is the error of MNS with the largest kernel size, which indicates that MD is a simplified MNS that has a kernel size of 1 pixel.
However, if considering lower blur levels (up to a kernel size of 5×5 pixels), which are probably more relevant in practice, the level of invariance is still worthwhile.
Unless stated otherwise a kernel size of 30 kb was used in this paper.
A noise-removal filter with a kernel size of 3 × 3 was applied to these three-dimensional images.
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The histograms were constructed from 10 min windows (140 probe stimuli), stepped by 1 min. A given histogram had a bin width of 0.04 ms, corresponding to the sampling frequency of 25 kHz, and was smoothed in latency with a Gaussian kernel size of 31 samples.
The kernel density maps were created using a spatial resolution of 100 meters and a search radius (kernel size) of 1000 meters via the software ArcGIS.
A 3D Gaussian with kernel size of 60 μm × 60 μm × 60 μm was then used to estimate the density of cells at each voxel.
All neurons from all animals were plotted in a reference space with 20 μm × 20 μm × 20 μm voxels, and a 3D Gaussian with kernel size of 60 μm × 60 μm × 60 μm was used to estimate density at each voxel.
For collagen signal density calculations, a noise removal filter with kernel size of 3 × 3 was used to define the boundary between foreground and background, and the lower threshold in the histogram was set to mean voxel intensity value.
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