Rendering fine hair-like objects with Gaussian noise

Shinya, Mikio; Nishida, Shin’ya

doi:10.1145/2628257.2628269

Cited by 1 publication

(1 citation statement)

References 4 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This question is related to long and unsettled debates as to whether the brain explicitly constructs representations for filled-on/filledout perceptions (Anderson & Van Essen, 1987;Komatsu, 2006;Boyaci, Fang, Murray, & Kersten, 2007;Hsieh & Tse, 2009). Finally, with regard to practical applications, the present findings support the view that in computer graphics, accurate, but high-cost, simulations of detailed object structures are unnecessary for image rendering of superfine structures (Shinya & Nishida, 2014).…”

Section: Discussionsupporting

confidence: 75%

Human perception of subresolution fineness of dense textures based on image intensity statistics

2017

Self Cite

View full text Add to dashboard Cite

We are surrounded by many textures with fine dense structures, such as human hair and fabrics, whose individual elements are often finer than the spatial resolution limit of the visual system or that of a digitized image. Here we show that human observers have an ability to visually estimate subresolution fineness of those textures. We carried out a psychophysical experiment to show that observers could correctly discriminate differences in the fineness of hair-like dense line textures even when the thinnest line element was much finer than the resolution limit of the eye or that of the display. The physical image analysis of the textures, along with a theoretical analysis based on the central limit theorem, indicates that as the fineness of texture increases and the number of texture elements per resolvable unit increases, the intensity contrast of the texture decreases and the intensity histogram approaches a Gaussian shape. Subsequent psychophysical experiments showed that these image features indeed play critical roles in fineness perception; i.e., lowering the contrast made artificial and natural textures look finer, and this effect was most evident for textures with unimodal Gaussian-like intensity distributions. These findings indicate that the human visual system is able to estimate subresolution texture fineness on the basis of diagnostic image features correlated with subresolution fineness, such as the intensity contrast and the shape of the intensity histogram.

show abstract