figure-ground

figure-ground

The perceptual difference between an object and its surroundings.
References in periodicals archive ?
Which figure-ground relationship do you believe works best?
These models are collected around ten areas of relevance for both fields, forming separate chapters: Sounding, Spot Elevation, Isobath/Contour, Hachure/Hatch, Shaded Relief, Land Classification, Figure-Ground, Stratigraphic Column, Cross Section, Line Symbol and Conventional Sign.
It presents figure-ground plans of the cities, with sections on the nine density categories, and offers many color photos of buildings, aerial color photos of neighborhoods, and color diagrams and maps.
figure-ground contrast, center bias and boundary cropping.
The surface of rough felt, on the one hand, and the granular, volcanic texture of the combustion, on the other, remain distinct (irregularities are visible only upon close examination) but removed from any hierarchical figure-ground relationship.
Behind this drama are backdrops of two or three large zones of color whose ambiguous figure-ground relationships underpin the more conspicuous activity up front.
Grossberg, "Neural dynamics of 3-D surface perception: figure-ground separation and lightness perception," Perception and Psychophysics, vol.
These tests assess visual attention (focused, shifting, and selective), visual scanning, visual sequencing, figure-ground perception, size and shape discrimination, visual matching, depth perception, visual organization, and visual-spatial processing speed.
Metaphor-as-gestalt has become one of the important postulates in cognitive poetics, although its understanding as well as the issue of figure-ground relationship may undergo different scholarly interpretations (Freeman 2000, 2009; Stockwell 2002; Tsur 2009, 2012).
Processes of lumping and splitting a national population and in particular the neutral character of the reference category become more revealing in the light of the Gestalt principle figure-ground or the figure-ground reversal (Gross and Harmon, 2014, Strathern, 2002).
For this purpose, SEED will develop novel high-order compositional methodologies for the semantic segmentation of video data acquired by observers of dynamic scenes, by adaptively integrating figure-ground reasoning based on bottom-up and top-down information, and by using weakly supervised machine learning techniques that support continuous learning towards an open-ended number of visual categories.
This figure-ground information would make it easier to judge how fast it was moving and whether you should turn or not.