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Theoretical models from [15] also confirm that noise variance in depth is larger than noise variance in luminance images.
Since the initial value of the noise variance in depth is significantly reduced after temporal filtering, we propose to use a modified initial estimate of the noise variance.
Accordingly, a one-second averaged background depth map image is obtained to reduce random variance in depth values for the same pixel.
Coverage depth was slightly higher for the duplicate class, while variance in depth of coverage was similar between these two categories (Additional file 5: Figure S3).
Key sources of non-uniformity are variance in depth of coverage, artifactual variants resulting from repetitive regions and larger structural variants.
> -wrap-foot> > -wrap-foot> To account for variance in depth across the data, features that scale with depth (e.g. feature x2 to x17) are first normalized by dividing by the depth.
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In case contact point 1 is placed at a depth that is only given by the MRI-based target, due to the variance in the depth of the STN, not all contact points will be necessarily situated inside the STN and, therefore, not the best functional target will be stimulated [ 8, 13, 23, 28, 32, 33].
As shown in [1, 8], noise variance in TOF depth sensors, among other factors, depends on the intensity of the emitted light, the reflectivity of the scene and the distance of the object in the scene.
We also investigate the case when the noise standard deviation input parameter is equal to the maximum value of the noise variance in the depth frame, i.e. 20, denoted as "Case2" in Figures 10 and 11.
Due to the inverse dependence of ΔA on the detected signal amplitude A and the fact that A is highly dependent on the reflectance and distance of objects, the noise variance in the depth scene is highly spatially variable.
In addition to beak length, females showed a significantly greater variance in beak depth (one-tailed F-test 20, 16 df; P = 0.012).
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