Getting “fumpered”: Classifying objects by what has been done to them

Fleming, Roland W.; Schmidt, Fritz

doi:10.1167/19.4.15

Cited by 19 publications

(23 citation statements)

References 36 publications

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“…One of the most important goals for biological and artificial vision is the estimation and representation of shape. Shape is the most important cue in object recognition [1][2][3][4] and is also crucial for many other tasks, including inferring an object's material properties [5][6][7][8][9], causal history [10][11][12][13], or where and how to grasp it [14][15][16][17][18]. Shape is also central to many other disciplines, including computational morphology [19], anatomy [20], molecular biology [21], geology [22], meteorology [23], computer vision [24], and computer graphics [25].…”

Section: Introductionmentioning

confidence: 99%

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An image-computable model of human visual shape similarity

Morgenstern

Hartmann

Schmidt

et al. 2020

Preprint

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Shape is a defining feature of objects. Yet, no image-computable model accurately predicts how similar or different shapes appear to human observers. To address this, we developed a model ('ShapeComp'), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp predicts human shape similarity judgments almost perfectly (r 2 >0.99) without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that human shape perception is inherently multidimensional and optimized for comparing natural shapes. ShapeComp outperforms conventional metrics, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.

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Section: Introductionmentioning

confidence: 99%

“…Although real-world objects are 3D, humans can make many inferences from 2D contours (e.g., [13,26,27]). Many 2D shape representations have been proposed-both for computational purposes and as models of human perception-each summarizing the shape boundary or its interior in different ways (Figure 1B; [24]).…”

Section: Introductionmentioning

confidence: 99%

An image-computable model of human visual shape similarity

Morgenstern

Hartmann

Schmidt

et al. 2020

Preprint

Self Cite

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show abstract

“…For example, localized surface deformations (such as indentations, cuts or spikes) are clearly at a larger scale than surface textures but not necessarily integral part of global object shape (e.g., think of an indented bumper bar). This article is thus closely related to investigations of localized transformations of two-dimensional contour or three-dimensional object shape ( Fleming & Schmidt, 2019 ; Kubilius, Bracci, & Op de Beeck, 2016 ; Op de Beeck, Torfs, & Wagemans, 2008 ; Pinna, 2010 ; Pinna & Deiana, 2015 ; Schmidt et al, 2019 ). These studies showed that observers can identify transformed regions of objects, and use them to make inferences about object classes, causal history, or material identity.…”

Section: Discussionmentioning

confidence: 99%

“…Here, we focus on local shape features, which is the level at which textiles form the distinctive ripples and creases of drapery and viscous liquids clump up into clots or dollops. Also, it is the scale at which some large-scale deformations become evident, such as when an object is bent or twisted ( Fleming & Schmidt, 2019 ; Schmidt & Fleming, 2016 , Schmidt & Fleming, 2018 ; Schmidt, Phillips, & Fleming, 2019 ; Schmidt, Spröte, & Fleming, 2016 ; Spröte & Fleming, 2016 ; Spröte, Schmidt & Fleming, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Softness and weight from shape: Material properties inferred from local shape features

2020

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Object shape is an important cue to material identity and for the estimation of material properties. Shape features can affect material perception at different levels: at a microscale (surface roughness), mesoscale (textures and local object shape), or megascale (global object shape) level. Examples for local shape features include ripples in drapery, clots in viscous liquids, or spiraling creases in twisted objects. Here, we set out to test the role of such shape features on judgments of material properties softness and weight. For this, we created a large number of novel stimuli with varying surface shape features. We show that those features have distinct effects on softness and weight ratings depending on their type, as well as amplitude and frequency, for example, increasing numbers and pointedness of spikes makes objects appear harder and heavier. By also asking participants to name familiar objects, materials, and transformations they associate with our stimuli, we can show that softness and weight judgments do not merely follow from semantic associations between particular stimuli and real-world object shapes. Rather, softness and weight are estimated from surface shape, presumably based on learned heuristics about the relationship between a particular expression of surface features and material properties. In line with this, we show that correlations between perceived softness or weight and surface curvature vary depending on the type of surface feature. We conclude that local shape features have to be considered when testing the effects of shape on the perception of material properties such as softness and weight.

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“…Previous research has shown that observers can determine and ‘discount’ the causal history of objects under some geometric shape transformations ( Schmidt & Fleming, 2016 ; Schmidt & Fleming, 2018 ; Schmidt, Spröte, & Fleming, 2016 ; Spröte & Fleming, 2016 ; Spröte, Schmidt, & Fleming, 2016 ; Fleming & Schmidt, 2019 ). For instance, Spröte and Fleming (2016) asked human observers to adjust the bending of a computer-simulated three-dimensional shape to match the degree of bending applied to another object.…”

Section: Introductionmentioning

confidence: 99%

Color consistency in the appearance of bleached fabrics

et al. 2020

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Human observers are remarkably good at perceiving constant object color across illumination changes. However, there are numerous other factors that can modulate surface appearance, such as aging, bleaching, staining, or soaking. Despite this, we are often able to identify material properties across such transformations. Little is known about how and to what extent we can compensate for the accompanying color transformations. Here we investigated whether humans could reproduce the original color of bleached fabrics. We treated 12 different fabric samples with a commercial bleaching product. Bleaching increased luminance and decreased saturation. We presented photographs of the original and bleached samples on a computer screen and asked observers to match the fabric colors to an adjustable matching disk. Different groups of observers produced matches for original and bleached samples. One group of observers were instructed to match the color of the bleached samples as they were before bleaching (i.e., compensate for the effects of bleaching); another, to accurately match color appearance. Observers did compensate significantly for the effects of bleaching when instructed to do so, but not in the appearance match condition. Results of a second experiment suggest that observers achieve color consistency, at least in part, through a strategy based on local spatial differences within the bleached samples. According to the results of a third experiment, these local spatial differences are likely to be the perceptual image cues that allow participants to determine whether a sample is bleached. When the effect of bleaching was limited or uniformly distributed across a sample's surface, observers were uncertain about the bleaching magnitude and seemed to apply cognitive strategies to achieve color consistency.

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Getting “fumpered”: Classifying objects by what has been done to them

Cited by 19 publications

References 36 publications

An image-computable model of human visual shape similarity

An image-computable model of human visual shape similarity

Softness and weight from shape: Material properties inferred from local shape features

Color consistency in the appearance of bleached fabrics

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