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Martin Giesel, Qasim Zaidi; Visual perception of material affordances. Journal of Vision 2011;11(11):356. doi: 10.1167/11.11.356.
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© ARVO (1962-2015); The Authors (2016-present)
Material perception may be as important as object perception for successful interactions with the environment. Possibly the most important facet of visual material identification is to infer properties from the available information that can provide predictions for the appearance of the material in other states, for effects of the material on other sensory modalities, for the calibration of motor actions, and most importantly for potential uses, i.e. affordances. We present an investigation of the inference of useful properties of fabrics from exclusively visual information. We used 261 color images of flat fabrics, presented on a monitor as similar sized squares surrounded by black. We asked observers to rate each image separately on four property dimensions anchored by the poles: soft – rough, stiff – flexible, warm – cool, and water-repellent – water-absorbent. To guide observers in inferring each property, we presented them with a question aimed at potential uses of the material. For data analysis, we used images that were rated consistently across repetitions and observers. We show that between 30% and 50% of the images rated by the observers as belonging to one property class were assigned consistently across observers. Based on verbal descriptions from the sources where we obtained the images, it seems that visual judgments of affordances are close to the ground truth, but to make more definite statements we will run physical and tactile tests on fabrics. Attempts at modeling fabrics have shown that since stitches/knits can be resolved visually, the surface cannot be assumed to be flat, so 2-D image-statistics and texture-mapping schemes are insufficient. As an alternative, we are using results that affordance groups like soft, flexible, and water-absorbent, and rough, stiff, and water-repellent, contain a large proportion of common images, to identify the critical perceptual qualities that underlie inferences of multiple material qualities.
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