Material category of visual objects computed from specular image structure
Abstract
There is a growing body of work investigating the visual perception of material properties like gloss,
yet practically nothing is known about how the brain recognizes different material classes like
plastic, pearl, satin, and steel, nor the precise relationship between material properties like gloss and
perceived material class. We report a series of experiments that show that parametrically changing
reflectance parameters leads to qualitative changes in material appearance beyond those expected
by the reflectance function used. We measure visual (image) features that predict these changes in
appearance, and causally manipulate these features to confirm their role in perceptual
categorization. Furthermore, our results suggest that the same visual features underlie both material
recognition and surface gloss perception. However, the predictiveness of each feature to perceived
gloss changes with material category, suggesting that the pockets of feature space occupied by
different material classes affect the processing of those very features when estimating surface
glossiness. Our results do not support a traditional feedforward view that assumes that material
perception proceeds from low-level image measurements, to mid-level estimates of surface
properties, to high-level material classes, nor the idea that material properties like gloss and
material class are simultaneously “read out” from visual gloss features. Instead, we suggest that the
perception and neural processing of material properties like surface gloss should be considered in
the context of material recognition.
Domains
Psychology
Origin : Files produced by the author(s)
