Color improves edge classification in human vision.

Camille Breuil,Ben J Jennings,Simon Barthelmé,Frederick A A Kingdom,Nathalie Guyader,Wolfgang Einhäuser

doi:10.1371/journal.pcbi.1007398

Abstract

Despite the complexity of the visual world, humans rarely confuse variations in illumination, for example shadows, from variations in material properties, such as paint or stain. This ability to distinguish illumination from material edges is crucial for determining the spatial layout of objects and surfaces in natural scenes. In this study, we explore the role that color (chromatic) cues play in edge classification. We conducted a psychophysical experiment that required subjects to classify edges into illumination and material, in patches taken from images of natural scenes that either contained or did not contain color information. The edge images were of various sizes and were pre-classified into illumination and material, based on inspection of the edge in the context of the whole image from which the edge was extracted. Edge classification performance was found to be superior for the color compared to grayscale images, in keeping with color acting as a cue for edge classification. We defined machine observers sensitive to simple image properties and found that they too classified the edges better with color information, although they failed to capture the effect of image size observed in the psychophysical experiment. Our findings are consistent with previous work suggesting that color information facilitates the identification of material properties, transparency, shadows and the perception of shape-from-shading.

Highlights

Edges are pervasive features of natural scenes
Understanding the role that each plays in our visual perception of natural scenes is a continuing topic of investigation
We explore the role that color cues play in a specific task: edge classification

Summary

Introduction

Edges are pervasive features of natural scenes. They can result from a number of causes: object occlusions, reflectance changes, texture changes, shading, cast shadows and highlights, to mention the main varieties. The first three of these constitute changes in material properties, while the last three, namely shading, cast shadows and highlights, constitute changes in the intensity of illumination. In the natural visual world color variations tend to be material in origin, whereas luminance variations tend to be either material or illumination, thereby privileging color over luminance as a potential cue for edge classification [4]. As a result the “color-is-material” assumption has been exploited by computer algorithms tasked with segmenting images of natural scenes into their material and illumination layers [5,6,7]. While there is evidence that human vision benefits from color in identifying edges [10], there is to date no psychophysical evidence that humans benefit from color when classifying edges

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