Image statistics and the perception of surface reflectance

Abstract

Humans are surprisingly good at judging the reflectance of complex surfaces even when the surfaces are viewed in isolation, contrary to the Gelb effect. We argue that textural cues are important for this task. Traditional machine vision systems, on the other hand, are incapable of recognizing reflectance properties. Estimating the reflectance of a complex surface under unknown illumination from a single image is a hard problem. Recent work in reflectance recognition has shown that certain statistics measured o an image of a surface are diagnostic of reflectance. We consider opaque surfaces with medium scale structure and spatially homogeneous reflectance properties. For such surfaces, we find that statistics of intensity histograms and histograms of filtered outputs are indicative of the diffuse surface reflectance. We compare the performance of a learning algorithm that employs these image statistics to human performance in two psychophysical experiments. In the first experiment, observers classify images of complex surfaces according to the perceived reflectance. We find that the learning algorithm rivals human performance at the classification task. In the second experiment, we manipulate the statistics of images and ask observers to provide reflectance ratings. In this case, the learning algorithm performs similarly to human observers. These findings lead us to conclude that the image statistics capture perceptually relevant information.