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

Abstract

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.

Visual discrimination of optical material properties: a large-scale study

“…This is why other materials related to dynamic information (reviewed by Nishida et al, 2018) related to the perception of liquidness (Kawabe et al, 2015), viscosity (Kawabe et al, 2015, van Assen & Fleming, 2018), stiffness (Paulun et al, 2017; Schmid & Doerschner, 2018), etc., were not used in the current investigation. In addition, the perception of wetness (Sawayama, Adelson, & Nishida, 2017) and the fineness of surface microstructures (Sawayama, Nishida, & Shinya, 2017) were not investigated because of the difficulty of continuously controlling physical material parameters by using identical geometries of other tasks. Since we only used five geometries, material perceptions derived from object mechanical properties were not investigated either (Schmidt et al, 2017).…”

Visual discrimination of optical material properties: A large-scale study

“…This is why other materials related to dynamic information (reviewed by Nishida, Kawabe, Sawayama, & Fukiage, 2018 ) related to the perception of liquidness ( Kawabe, Maruya, & Nishida, 2015 ), viscosity ( Kawabe, Maruya, Fleming, & Nishida, 2015 , van Assen & Fleming, 2016 ), and stiffness (Paulun et al., 2017; Schmid & Doerschner, 2018 ), among others, were not used in the current investigation. In addition, the perception of wetness ( Sawayama et al., 2017a ) and the fineness of surface microstructures ( Sawayama, Nishida, & Shinya, 2017b ) were not investigated because of the difficulty of continuously controlling physical material parameters by using identical geometries of other tasks. Because we only used five geometries, material perceptions derived from object mechanical properties were also not investigated ( Schmidt, Paulun, van Assen, & Fleming, 2017 ).…”

Scale ambiguities in material recognition

“…Yet, for a given material category, there may be certain cues iScience Article that are particularly important. For example, Sawayama et al, 2017 have shown that participants can judge the fineness of fibrous textures (like human hair), even when individual texture elements are smaller than the resolution of the imaging system. The main cue driving the super-resolution judgments in their displays was contrast: lower contrast patterns appeared to contain finer elements because of averaging of texture elements within each pixel.…”