Template model for blur coding: the role of early nonlinearity in edge segmentation

Gillian S. Barbieri-Hesse, Mark A. Georgeson

Research output: Unpublished contribution to conferenceOtherpeer-review


We describe a template model for perception of edge blur and identify a crucial early nonlinearity in this process. The main principle is to spatially filter the edge image to produce a 'signature', and then find which of a set of templates best fits that signature. Psychophysical blur-matching data strongly support the use of a second-derivative signature, coupled to Gaussian first-derivative templates. The spatial scale of the best-fitting template signals the edge blur. This model predicts blur-matching data accurately for a wide variety of Gaussian and non-Gaussian edges, but it suffers a bias when edges of opposite sign come close together in sine-wave gratings and other periodic images. This anomaly suggests a second general principle: the region of an image that 'belongs' to a given edge should have a consistent sign or direction of luminance gradient. Segmentation of the gradient profile into regions of common sign is achieved by implementing the second-derivative 'signature' operator as two first-derivative operators separated by a half-wave rectifier. This multiscale system of nonlinear filters predicts perceived blur accurately for periodic and aperiodic waveforms. We also outline its extension to 2-D images and infer the 2-D shape of the receptive fields.
Original languageEnglish
Publication statusUnpublished - 2002
Event25th European Conference on Visual Perception - Glasgow , United Kingdom
Duration: 25 Aug 200229 Aug 2002


Conference25th European Conference on Visual Perception
CountryUnited Kingdom
Internet address

Bibliographical note

Abstract published in ECVP 2002 Abstract Supplement, Perception, (August 2002, 1990) 13 (Supplement), p.54, 0301-0066.


  • perception
  • edge blur
  • early nonlinearity
  • spatially filter
  • blur-matching data
  • second-derivative signature
  • Gaussian first-derivative templates
  • Gaussian edges
  • non-Gaussian edges
  • receptive fields


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