From filters to features: Scale-space analysis of edge and blur coding in human vision

Mark A. Georgeson*, Keith A. May, Tom C. A. Freeman, Gillian S. Hesse

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.

Original languageEnglish
Article number7
Pages (from-to)1-21
Number of pages21
JournalJournal of Vision
Issue number13
Publication statusPublished - 19 Oct 2007

Bibliographical note

Creative Commons Attribution Non-Commercial No Derivatives License


  • blur
  • edges
  • feature analysis
  • Gaussian derivatives
  • half-wave rectification
  • human vision
  • scale-space theory
  • spatial filters
  • visual cortex


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