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Four classes of sites are assumed in the model and codons are categorized into these site classes based on foreground and background estimates of ω.
Fig. 5 Scoring based on foreground and foreground disparity images.
Figure 16 Foreground segmentation in colour images based on foreground detection of 3D points.
It's also not quite aggressive enough on foreground objects — those should blur too but only sometimes do.
Selection based on foreground, background and morphological traits was practiced from BC1F1 onwards for identification of lines that were similar to the recurrent parent.
Figure 16 shows foreground segmentation in the colour image based on foreground detection of 3D points in the world coordinate system.
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Cinema, on the other hand, is hellbent on foregrounding, on sucking people out of their mires of circumstance and setting them up for show.
To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground.
Other studies [9, 15], on the other hand, emphasize on separating foreground from background, estimating depths individually, and then combining them into a single depth image.
Figure 7 shows a (darkened) image in the background and the feature responses on all possible gaze locations on the foreground.
In the null model, ω1 is fixed equal to 1 on the foreground branch, whereas ω0 is estimated on the remaining branches.
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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