Color improves edge classification in human vision

• Published in: PLoS computational biology 2019-10, Vol.15 (10), p.e1007398
• Main Authors: Breuil, Camille, Jennings, Ben J, Barthelmé, Simon, Guyader, Nathalie, Kingdom, Frederick A A
• Format: Article
• Language: English
• Subjects: Adult; Biology and Life Sciences; Categorization (Psychology); Classification; Color; Color Perception - physiology; Computer and Information Sciences; Contrast Sensitivity - physiology; Cues; Engineering and Technology; Female; Form Perception - physiology; Funding; Human performance; Humans; Illumination; Image classification; Image processing; Inspection; International conferences; Life Sciences; Lighting; Male; Material properties; Neurons and Cognition; Neurosciences; Pattern recognition; Pattern Recognition, Visual - physiology; Photic Stimulation - methods; Physical Sciences; Psychological aspects; Psychological research; Psychophysics; Research and Analysis Methods; Shading; Shadows; Social Sciences; Software; Visual perception; Visual Perception - physiology; Wavelet transforms
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007398

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.