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

Gillian S. Barbieri-Hesse, Mark A. Georgeson

Research output: Contribution to conferenceOther

Abstract

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
http://ecvp.psy.gla.ac.uk/

Conference

Conference25th European Conference on Visual Perception
CountryUnited Kingdom
CityGlasgow
Period25/08/0229/08/02
Internet address

Bibliographical note

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

Keywords

  • 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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