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: Contribution to conferenceOther

Abstract

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 Many of the latter have inevitably been superseded, but the approach was inspirational and remains so.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
http://ecvp.psy.gla.ac.uk/

Conference

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

Fingerprint

filter
detection

Bibliographical note

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

Keywords

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

Cite this

Georgeson, M. A., Barbieri-Hesse, G. S., & Freeman, T. C. A. (2002). The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering. 25th European Conference on Visual Perception, Glasgow , United Kingdom.
Georgeson, Mark A. ; Barbieri-Hesse, Gillian S. ; Freeman, T.C.A. / The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering. 25th European Conference on Visual Perception, Glasgow , United Kingdom.
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Georgeson, MA, Barbieri-Hesse, GS & Freeman, TCA 2002, 'The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering', 25th European Conference on Visual Perception, Glasgow , United Kingdom, 25/08/02 - 29/08/02.

The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering. / Georgeson, Mark A.; Barbieri-Hesse, Gillian S.; Freeman, T.C.A.

2002. 25th European Conference on Visual Perception, Glasgow , United Kingdom.

Research output: Contribution to conferenceOther

TY - CONF

T1 - The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering

AU - Georgeson, Mark A.

AU - Barbieri-Hesse, Gillian S.

AU - Freeman, T.C.A.

N1 - Abstract published in ECVP 2002 Abstract Supplement, Perception, (August 2002, 1990) 13 (Supplement), p.1, 0301-0066.

PY - 2002

Y1 - 2002

N2 - 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]

AB - 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]

KW - vision

KW - Marr

KW - computational study

KW - psychophysics

KW - neurophysiology

KW - feature detection

KW - early human vision

KW - edges

KW - edge location

KW - blur

KW - Gaussian-derivative

KW - second-derivative profile

M3 - Other

ER -

Georgeson MA, Barbieri-Hesse GS, Freeman TCA. The primal sketch revisited: locating and representing edges in human vision via Gaussian-derivative filtering. 2002. 25th European Conference on Visual Perception, Glasgow , United Kingdom.