Color consistency in the appearance of bleached fabrics

Toscani, Matteo; Milojevic, Zarko; Fleming, Roland W.; Gegenfurtner, Karl R.

doi:10.1167/jov.20.4.11

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…uncooked shrimp is usually perceived to be more translucent). Previous works find that color interacts with the perception of surface gloss ( Nishida, Motoyoshi, & Maruya, 2011 ; Xiao & Brainard, 2008 ), perception of fabrics ( Toscani, Milojevic, Fleming, & Gegenfurtner, 2020 ; Xiao, Bi, Jia, Wei, & Adelson, 2016 ), perception of transparent objects ( D'Zmura, Colantoni, Knoblauch, & Laget, 1997 ; Ennis & Doerschner, 2021 ), and perception of object states such as wet or bleached ( Okawa, Shimano, Asano, Bise, Nishino, & Sato, 2019 ; Sawayama, Adelson, & Nishida, 2017 ; Toscani et al, 2020 ). On the other hand, a previous study on material classification from photographs using the Flickr Material Dataset finds that removing color does not significantly affect classification accuracy ( Sharan, Rosenholtz, & Adelson, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Crystal or jelly? Effect of color on the perception of translucent materials with photographs of real-world objects

2022

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Translucent materials are ubiquitous in nature (e.g. teeth, food, and wax), but our understanding of translucency perception is limited. Previous work in translucency perception has mainly used monochromatic rendered images as stimuli, which are restricted by their diversity and realism. Here, we measure translucency perception with photographs of real-world objects. Specifically, we use three behavior tasks: binary classification of “translucent” versus “opaque,” semantic attribute rating of perceptual qualities (see-throughness, glossiness, softness, glow, and density), and material categorization. Two different groups of observers finish the three tasks with color or grayscale images. We find that observers’ agreements depend on the physical material properties of the objects such that translucent materials generate more interobserver disagreements. Further, there are more disagreements among observers in the grayscale condition in comparison to that in the color condition. We also discover that converting images to grayscale substantially affects the distributions of attribute ratings for some images. Furthermore, ratings of see-throughness, glossiness, and glow could predict individual observers’ binary classification of images in both grayscale and color conditions. Last, converting images to grayscale alters the perceived material categories for some images such that observers tend to misjudge images of food as non-food and vice versa. Our result demonstrates that color is informative about material property estimation and recognition. Meanwhile, our analysis shows that mid-level semantic estimation of material attributes might be closely related to high-level material recognition. We also discuss individual differences in our results and highlight the importance of such consideration in material perception.

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Section: Introductionmentioning

confidence: 99%

Crystal or jelly? Effect of color on the perception of translucent materials with photographs of real-world objects

2022

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“…1). Interestingly, there is good evidence that we can not only recognize objects across such transformations but also recognize transformations across objects: We can infer growth or aging processes, crumpling, or folding from the associated changes in object shapes (e.g., Chen & Scholl, 2016;Kourtzi & Shiffrar, 1999, 2001Mark & Todd, 1985;Pinna, 2010;Pittenger & Todd, 1983;Schmidt & Fleming, 2018;Toscani et al, 2020;Yoonessi & Zaidi, 2010). One recent hypothesis about how this is achieved is "shape scission," which assumes that observers can distinguish between shape features that "belong" to the object and those that "belong" to the transformation Phillips & Fleming, 2020;Schmidt et al, 2019;.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, explicit tests of transformation recognition examined observers' interpretations of particular changes in object shape to specific transformations. For example, observers can infer complex "happenings" from line drawings of simple squares (Pinna, 2010), growth or ageing from changes in body proportions or head shape (Mark & Todd, 1985;Pittenger & Todd, 1983;, and transformations like bleaching, burning, decaying, folding, bending, crumpling, or twisting from photographs of real objects (Schmidt & Fleming, 2018;Toscani et al, 2020;Yoonessi & Zaidi, 2010).…”

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confidence: 99%

Inferring shape transformations in a drawing task

Schmidt,

Tiedemann,

Fleming

et al. 2023

Mem Cogn

Self Cite

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Many objects and materials in our environment are subject to transformations that alter their shape. For example, branches bend in the wind, ice melts, and paper crumples. Still, we recognize objects and materials across these changes, suggesting we can distinguish an object’s original features from those caused by the transformations (“shape scission”). Yet, if we truly understand transformations, we should not only be able to identify their signatures but also actively apply the transformations to new objects (i.e., through imagination or mental simulation). Here, we investigated this ability using a drawing task. On a tablet computer, participants viewed a sample contour and its transformed version, and were asked to apply the same transformation to a test contour by drawing what the transformed test shape should look like. Thus, they had to (i) infer the transformation from the shape differences, (ii) envisage its application to the test shape, and (iii) draw the result. Our findings show that drawings were more similar to the ground truth transformed test shape than to the original test shape—demonstrating the inference and reproduction of transformations from observation. However, this was only observed for relatively simple shapes. The ability was also modulated by transformation type and magnitude but not by the similarity between sample and test shapes. Together, our findings suggest that we can distinguish between representations of original object shapes and their transformations, and can use visual imagery to mentally apply nonrigid transformations to observed objects, showing how we not only perceive but also ‘understand’ shape.

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“…uncooked shrimp is usually perceived to be more translucent). Previous works find color interacts with perception of surface gloss (Xiao & Brainard,2008;Nishida, Motoyoshi, & Maruya,2011), perception of fabrics (Xiao, Bi, Jia, Wei, & Adelson,2016;Toscani, Milojevic, Fleming, & Gegenfurtner,2020), perception of transparent objects (D'Zmura, Colantoni, Knoblauch, & Laget,1997;Ennis & Doerschner,2021), and perception of object states such as wet or bleached (Sawayama, Adelson, & Nishida,2017;Okawa et al,2019;Toscani et al,2020). On the other hand, a previous study on material classification from photographs using the Flickr Material Dataset finds removing color does not significantly affect classification accuracy (Sharan, Rosenholtz, & Adelson,2014).…”

Section: Introductionmentioning

confidence: 99%

Crystal or Jelly? Effect of Color on the Perception of Translucent Materials with Photographs of Real-world Objects

Liao

Sawayama²,

Xiao

2021

Preprint

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Translucent materials are ubiquitous in nature (e.g. teeth, food, wax), but our understanding of translucency perception is limited. Previous work in translucency perception has mainly used monochromatic rendered images as stimuli, which are restricted by their diversity and realism. Here, we measure translucency perception with photographs of real-world objects. Specifically, we use three behavior tasks: binary classification of 'translucent' versus 'opaque', semantic attribute rating of perceptual qualities (see-throughness, glossiness, softness, glow and density), and material categorization. Two different groups of observers finish the three tasks with color or grayscale images. We find that observers' agreements depend on the physical material properties of the objects such that translucent materials generate more inter-observer disagreements. Further, there are more disagreements among observers in the grayscale condition in comparison to that in color condition. We also discover that converting images to grayscale substantially affects the distributions of attribute ratings for some images. Furthermore, ratings of see-throughness, glossiness, and glow could predict individual observers' binary classification of images in both grayscale and color conditions. Lastly, converting images to grayscale alters the perceived material categories for some images such that observers tend to misjudge images of food as non-food and vice versa. Our result demonstrates color is informative about material property estimation and recognition. Meanwhile, our analysis shows mid-level semantic estimation of material attributes might be closely related to high-level material recognition. We also discuss individual differences in our results and highlight the importance of such consideration in material perception.

show abstract

Color consistency in the appearance of bleached fabrics

Cited by 6 publications

References 40 publications

Crystal or jelly? Effect of color on the perception of translucent materials with photographs of real-world objects

Crystal or jelly? Effect of color on the perception of translucent materials with photographs of real-world objects

Inferring shape transformations in a drawing task

Crystal or Jelly? Effect of Color on the Perception of Translucent Materials with Photographs of Real-world Objects

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