Sentence examples for spurious features from inspiring English sources
The phrase "spurious features" is correct and usable in written English
It is typically used in a technical or scientific context to describe features that appear to be real, but are actually false or misleading. For example, "The results of the experiment have been complicated by spurious features introduced by the measurement device."
If this correction is not properly determined, spurious features can be introduced.
The latter however displayed spurious features attributed to the inconsistent treatment of different physical waves.
Solutions obtained with very high polynomial orders displayed spurious features attributed to a sharper dissipation in wavenumber space.
A morphological filtering algorithm opens up holes and gaps in a mask to get rid of small and potentially spurious features.
Sinks in digital elevation models (DEMs) can be existing features that correctly represent depressions in actual landscapes or spurious features that result from errors in DEM creation.
For instance, with these extreme magnetisations, for GLM we observe spurious features near the poles at (theta=0) and (theta=pi), as well as deviations in the velocity field near the outer boundary (r=10mbox{ M}).
Poor quality fingerprint images can lead to incorrect or spurious feature (minutia) detection (illustrated in Figure8) and thereby degrading the performance of a fingerprint recognition system.
Even though coherency is above the background level, part of this northeastern finger may be a spurious feature, and that the actual northeastern rupture may stop at about 39°N.
We show that, in the typical regimes in which the application of multilayer shallow water models is justified, the resulting discretization does not introduce any major spurious feature and allows again to reduce substantially the computational cost in areas with complex bathymetry.
DOI: http://dx.doi.org/10.7554/eLife.02020.004 10.7554/eLiFigure20.005 Figure 1 figure supplement 2. Phenotypic vs spurious feature variation in Clinical Face Phenotype Space using simulated faces.
Our deformable surface model is more efficient, much less sensitive to initialization and spurious image features, more proficient in extracting boundary concavities, and not susceptible to self-intersections compared to most other models of its type.
