Relative contributions of low- and high-luminance components to material perception

Abstract

Besides specular highlights, image pixels that represent clues to recognizing the object material, such as shading between threads of fabrics, often yield relatively lower luminance in the image. Here, we psychophysically examined how lower and higher luminance components contribute to material perception. We created two types of luminance-modulated images-low-and highluminance-preserved (LLP and HLP) images-and instructed observers to choose which modified image resulted in a material impression closer to the original. LLP images were created by compressing the luminance contrast of the higher half of the histogram in each original photograph and vice versa. The stimuli were photographs of various samples of stone, wood, leather, and fabric. Although the LLP and HLP images were equally chosen, the choice ratios of the HLP images largely differed across the samples and categories and moderately correlated with the luminance statistics of higher-spatial-frequency subbands. These results suggest that either the lower-or higher-luminance components play an important role in material perception, depending on the material category. However, the correlation with sub-band image statistics for stone/wood samples was much weaker than for leather/fabric samples, suggesting that more intricate image characteristics may be involved in evaluating the material impressions of the stone/wood samples.

Material category of visual objects computed from specular image structure

“…Within this framework it would make sense to first study how the visual system computes mid-level material properties like surface gloss and colour from images, because material class is thought to be subsequently computed from the high-dimensional feature space defined by these component mid-level properties [9][10][11][12]27,28 .…”

“…8). On the other hand, the 0.5 Uncoloured metals (1) Ceramics (2) Rubber-like (3) Gold metals (4) Velvety/silky (5) Pearlescent ( 6) Unglazed porcelain (7) Solid chocolate (8) Glazed porcelain (9) Plastic (10) Melted chocolate (11) Brown metals ( 12 Uncoloured metals (1) Ceramics (2) Rubber-like (3) Gold metals (4) Velvety/silky (5) Pearlescent (6) Unglazed porcelain (7) Solid chocolate (8) Glazed porcelain (9) Plastic (10) Melted chocolate (11) Brown metals ( 12 After applying simple feature manipulations, materials sometimes resembled unintended categories (red arrows). The perceived materials align much better with the intended category (green arrows) after the complex feature manipulations.…”

“…Although there is a growing body of work investigating the visual perception of material properties such as colour, lightness, transparency, translucency and gloss [1][2][3][4][5][6] , there is comparatively little investigating the recognition of different material classes such as plastic, pearl, satin, steel and so on [7][8][9][10][11][12][13][14][15][16][17] . For example, previous research has discovered a limited set of image conditions (photogeometric constraints) that trigger the perception of a glossy versus a matte surface, involving the intensity, shape, position and the orientation of specular highlights (bright reflections [18][19][20][21][22][23][24] ) and lowlights (dark reflections 25 ) with respect to diffuse shading.…”

Material category of visual objects computed from specular image structure

“…While there is a growing body of work investigating the visual perception of material properties like colour, lightness, transparency, translucency, and gloss (for reviews see Anderson, 2011, 2020; Foster, 2011; Chadwick & Kentridge, 2015; Fleming 2014, 2017), there is comparatively little work investigating the recognition of different material classes like plastic, pearl, satin, steel, etc. (Balas, 2017; Baumgartner et al, 2013; Fleming et al, 2013; Lagunas et al, 2021; Nagai et al, 2015; 2018; Norman et al, 2020; Sharan et al, 2014; Tamura et al, 2018; Todd & Norman, 2018, 2019; Wiebel et al, 2013). For example, previous research has discovered a limited set of image conditions (photogeometric constraints) that trigger the perception of a glossy versus matte surface, involving the intensity, shape, position, and orientation of specular highlights (bright reflections; Beck & Prazdny, 1981; Blake & Bülthoff, 1990; Wendt et al, 2008; Todd et al, 2004; Anderson & Kim, 2009; Kim et al, 2011; Marlow et al, 2011), and lowlights (dark reflections; Kim et al, 2012) with respect to diffuse shading.…”

“…Furthermore, a traditional feedforward view of neural processing is often assumed in which the recognition of objects and materials proceeds from the processing of low-level sensory information (image cues) to the estimation of shape and surface properties (often referred to as mid-level vision; Anderson, 2011, 2020) to the high-level recognition of object and material categories ( feedforward hypothesis ; Figure 1B (i); e.g., Komatsu & Goda, 2018). Within this framework it would make sense to first study how the visual system computes mid-level material properties like surface gloss and colour from images, as material class is thought to be subsequently computed from the high-dimensional feature space defined by these component mid-level properties (e.g., Fleming et al, 2013; Lagunas, 2021; Nagai et al, 2015, 2018; Schwartz & Nishino, 2020; Tanaka & Horiuchi, 2015).…”