![]() The widths of these classification images tracked the widths of the edges, but the chromatic edge classification images were wider than the luminance ones. We found classification images for both luminance and isoluminant chromatic stimuli that had shapes very similar to derivatives of Gaussian filters. ![]() 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. ![]() Classification image analysis was applied to observer responses. Edges and noise were defined by either luminance or chromatic contrast (isoluminant L/M and S-cone opponent). Brown noise was used in preference to white noise to reveal localized edge detectors. Observers had to choose which of two stimuli contained the edge. 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. ![]() 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |