Curved reflection symmetry detection with self-validation

Jingchen Liu, Yanxi Liu

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

11 Scopus citations


We propose a novel, self-validating approach for detecting curved reflection symmetry patterns from real, unsegmented images. Our method benefits from the observation that any curved symmetry pattern can be approximated by a sequence of piecewise rigid reflection patterns. Pairs of symmetric feature points are first detected (including both inliers and outliers) and treated as 'particles'. Multiple-hypothesis sampling and pruning are used to sample a smooth path going through inlier particles to recover the curved reflection axis. Our approach generates an explicit supporting region of the curved reflection symmetry, which is further used for intermediate self-validation, making the detection process more robust than prior state-of-the-art algorithms. Experimental results on 200+ images demonstrate the effectiveness and superiority of the proposed approach.

Original languageEnglish (US)
Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages13
EditionPART 4
StatePublished - 2011
Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 12 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume6495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th Asian Conference on Computer Vision, ACCV 2010
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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