Contour extrapolation using probabilistic cue combination

Manish Singh, Jacqueline M. Fulvio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A common approach to the problem of contour interpolation is based on the calculus of variations. The optimal interpolating contour is taken to be one that minimizes a given smoothness functional. Two important such functionals are total curvature (or bending energy) and variation in curvature. We analyzed contours extrapolated by human observers given arcs of Euler spirals that disappeared behind an occluding surface. Irrespective of whether the Euler spirals had increasing or decreasing curvature as they approached the occluding edge, visually-extrapolated contours were found to be characterized by decaying curvature. This curvature decay is modeled in terms of a Bayesian interaction between probabilistically-expressed constraints to minimize curvature and minimize variation in curvature. The analysis suggests that using fixed smoothness functionals is not appropriate for modeling human vision. Rather, the relative weights assigned to different probabilistic shape constraints may vary as a function of distance from the point(s) of occlusion. Implications are discussed for computational models of shape completion.

Original languageEnglish (US)
Title of host publication2006 Conference on Computer Vision and Pattern Recognition Workshop
DOIs
StatePublished - 2006
Event2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2006
ISSN (Print)1063-6919

Other

Other2006 Conference on Computer Vision and Pattern Recognition Workshops
Country/TerritoryUnited States
CityNew York, NY
Period6/17/066/22/06

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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