Sentence examples for background representations from inspiring English sources

Each tiny wave sound impresses itself on our sensory organs and are then combined into a more noticeable sound, one that will stand out from the rest of the background representations of the world.

A background subtraction approach usually considers three main issues: background representation, background updating, and background initialization [1].

For the popular background subtraction approach called the Gaussian background model, Stauffer and Grimson [4] presented a pixel-wise background representation scheme using the mixture of Gaussians (MoG) and pixel-wise background updating to update the intensity mean and variance of each pixel in real-time.

The significance of enrichment of ARE-containing transcripts among the escapees over the background representation in all the genes on the array was calculated by assuming a hypergeometric distribution of ARE elements in 3' UTRs.

Using MEME (Multiple Em for Motif Elicitation [46]), several conserved elements were detected in these 3' UTRs; however, none of these was enriched among the escapees when compared to the background representation in the 3' UTRs of all the genes on the array (data not shown).

The later methods often incorporate better background representation to account for the non-random properties of sequences.

Therefore the expected background representation ratio, or the random representation ratio for G1 within G2 is, and the observed representation ratio for G1 within G2 is, EF is then defined as, with a value for EF > 1 indicating higher than background representation ratio for G1 within G2.

In the HTML representation, the result table contains both text and background color representations of the genotype data for the selected strains.

Departing from a Swedish higher education and research policy landscape, the aim of this article is to explore how, in comparison with their Swedish colleagues, women academics with a migrant background make representations of the good researcher in their work practices.

Further, we design a discriminative criterion of the likelihood for target location, which simultaneously considers the target, background and representation errors.

For instance, in practice it is hard to set a decision threshold between the object and background with representation error [4,16,18,19].