Evidence for chromatic edge detectors in human vision using classification images.

Edge detection plays an important role in human vision, and although it is clear that there are luminance edge detectors, it is not known whether there are chromatic edge detectors as well. We showed observers a horizontal edge blurred by a Gaussian filter (with widths of σ = 0.1125, 0.225, or 0.45°) embedded in blurred Brown noise. Observers had to choose which of two stimuli contained the edge. Brown noise was used in preference to white noise to reveal localized edge detectors. Edges and noise were defined by either luminance or chromatic contrast (isoluminant L/M and S-cone opponent). Classification image analysis was applied to observer responses. In this analysis, the random components of the stimulus are correlated with observer responses to reveal a template that shows how observers weighted different parts of the stimulus to arrive at their decision. We found classification images for both luminance and isoluminant chromatic stimuli that had shapes very similar to derivatives of Gaussian filters. The widths of these classification images tracked the widths of the edges, but the chromatic edge classification images were wider than the luminance ones. These results are consistent with edge detection filters sensitive to luminance contrast and isoluminant chromatic contrast.