Show simple item record

dc.contributor.authorMcIlhagga, William H.*
dc.contributor.authorMay, K.A.*
dc.date.accessioned2014-04-28T10:55:36Z
dc.date.available2014-04-28T10:55:36Z
dc.date.issued2012
dc.identifier.citationMcIlhagga WH, May KA (2012) Optimal edge filters explain human blur detection. Journal of Vision. 12(10), 9, 1-13.
dc.identifier.urihttp://hdl.handle.net/10454/6091
dc.description.abstractEdges are important visual features, providing many cues to the three-dimensional structure of the world. One of these cues is edge blur. Sharp edges tend to be caused by object boundaries, while blurred edges indicate shadows, surface curvature, or defocus due to relative depth. Edge blur also drives accommodation and may be implicated in the correct development of the eye's optical power. Here we use classification image techniques to reveal the mechanisms underlying blur detection in human vision. Observers were shown a sharp and a blurred edge in white noise and had to identify the blurred edge. The resultant smoothed classification image derived from these experiments was similar to a derivative of a Gaussian filter. We also fitted a number of edge detection models (MIRAGE, N(1), and N(3)(+)) and the ideal observer to observer responses, but none performed as well as the classification image. However, observer responses were well fitted by a recently developed optimal edge detector model, coupled with a Bayesian prior on the expected blurs in the stimulus. This model outperformed the classification image when performance was measured by the Akaike Information Criterion. This result strongly suggests that humans use optimal edge detection filters to detect edges and encode their blur.
dc.subjectBlur perception; Contrast Sensitivity; Physiology
dc.subject; Classification images; Cues; Edge detection
dc.subject; Humans
dc.subject; Models; Theoretical
dc.subject; Photic stimulation
dc.subject; Psychophysics
dc.subject; Visual perception; Physiology
dc.subject; REF 2014
dc.titleOptimal edge filters explain human blur detection
dc.typeArticle
dc.identifier.doihttps://doi.org/10.1167/12.11.7


This item appears in the following Collection(s)

Show simple item record