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Different from 2D saliency prediction, stereoscopic saliency detection methods have to consider depth factor.
The depth factor had the most important effect over the growth on the longline with HDPE tubes system (p < 0.001).
To obtain the empirical relationship between the permeability coefficient and the depth factor, experimental equipment was built to reproduce existing conditions in a silo.
As the depth factor relates to the percent of root coverage in the water column, the aforementioned negative correlation for TP, TN and NH4+-N with depth could be interpreted as the rhizosphere effect.
According to the above analysis, the key issue for a 3D visual saliency prediction model is how to adopt the depth factor and how to combine the depth factor with 2D information based on the mechanisms of HVS.
In contrast to saliency detection for 2D images, the depth factor must be considered when performing saliency detection for RGB-D images.
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The optimum (reference) depth of 200 mm has been derived accompanying with corresponding depth factors for calibrating both the strength and modulus of elasticity tested with other depths between 50 and 250 mm.
This type of model considers the characteristics of depth factors and color information.
Level 4 builds on a level 3 complexity with an added sawgrass interaction factor, much like the soil phosphorus and depth factors.
Separate ANOVA models were then used to analyze the combined effects of culture system, season, and sampling depth factors on maximum water temperature stratification, minimum DO, DO stratification, and mean TAN.
Wang et al. [17] proposed a computational model that takes the depth factors as an additional visual dimension and provides a public database with a ground truth of eye-tracking data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com