The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering

Mark A. Georgeson, Gillian S. Barbieri-Hesse, T.C.A. Freeman

Research output: Unpublished contribution to conferenceOtherpeer-review


Marr's work offered guidelines on how to investigate vision (the theory - algorithm - implementation distinction), as well as specific proposals on how vision is done. Many of the latter have inevitably been superseded, but the approach was inspirational and remains so. Marr saw the computational study of vision as tightly linked to psychophysics and neurophysiology, but the last twenty years have seen some weakening of that integration. Because feature detection is a key stage in early human vision, we have returned to basic questions about representation of edges at coarse and fine scales. We describe an explicit model in the spirit of the primal sketch, but tightly constrained by psychophysical data. Results from two tasks (location-marking and blur-matching) point strongly to the central role played by second-derivative operators, as proposed by Marr and Hildreth. Edge location and blur are evaluated by finding the location and scale of the Gaussian-derivative `template' that best matches the second-derivative profile (`signature') of the edge. The system is scale-invariant, and accurately predicts blur-matching data for a wide variety of 1-D and 2-D images. By finding the best-fitting scale, it implements a form of local scale selection and circumvents the knotty problem of integrating filter outputs across scales. [Supported by BBSRC and the Wellcome Trust]
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
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note

Abstract published in ECVP 2002 Abstract Supplement, Perception, (August 2002, 1990) 13 (Supplement), p.1, 0301-0066.


  • vision
  • Marr
  • computational study
  • psychophysics
  • neurophysiology
  • feature detection
  • early human vision
  • edges
  • edge location
  • blur
  • Gaussian-derivative
  • second-derivative profile


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