Symmetry Continuous Feature

Hagit Zabrodsky, Shmuel Peleg, David Avnir

Research output: Contribution to journalArticlepeer-review

305 Scopus citations

Abstract

Symmetry is treated as a continuous feature and a Continuous Measure of Distance from Symmetry in shapes is defined. The Symmetry Distance (SD) of a shape is defined to be the minimum mean squared distance required to move points of the original shape in order to obtain a symmetrical shape. This general definition of a symmetry measure enables a comparison of the “amount” of symmetry of different shapes and the “amount” of different symmetries of a single shape. This measure is applicable to any type of symmetry in any dimension. The Symmetry Distance gives rise to a method of reconstructing symmetry of occluded shapes. We extend the method to deal with symmetries of noisy and fuzzy data. Finally, we consider grayscale images as 3D shapes, and use the Symmetry Distance to find the orientation of symmetric objects from their images, and to find locally symmetric regions in images.

Original languageEnglish
Pages (from-to)1154-1166
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume17
Issue number12
DOIs
StatePublished - Dec 1995

Keywords

  • Symmetry
  • face orientation
  • fuzzy shapes
  • local symmetry
  • occlusion
  • similarity measure
  • symmetry distance

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