Here's an example: Background blur depends on your depth of field.
This approach is useful, as every dataset contains many elements which cannot be part of a fiber, for example background voxels and low valued image noise.
For example, background motion artifacts that are caused by trees or additional persons may degrade the silhouette qualities that are required for gait biometrics, and illumination changes caused by cloud cover could dramatically change head textures.
The motion segmentation approach is used in each image plane, for example, background subtraction is used to detect a moving object to obtain a measurement for the local estimate.
To take that into account and to include the noise term from the equation, for example background noise and wind noise, DT (including noise) was set to +20 dB.
For example, background levels of hybridization (i.e. hybridization to a probe that occurs irrespective of the corresponding transcript's expression level) limit the accuracy of microarray expression measurements, particularly for transcripts present at low abundance.
For example, background signaling is due to trans-cleavage during transient, diffusive receptor encounters.
Occupational exposures that did not exceed the non-occupational background level were omitted for example, background radiation levels).
For example, background correction is handled completely differently using individual algorithms (Table S2 in Additional file 1).
For example, background processes and optional activities or tasks are penalized with a factor (PenaltyNonUser, PenaltyOptionalActivity and PenaltyOptionalTask, respectively).
