Curved reflection symmetry detection with self-validation

Jingchen Liu, Yanxi Liu

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

10 Citations (Scopus)

Abstract

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
Pages102-114
Number of pages13
EditionPART 4
DOIs
StatePublished - Mar 16 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

Other

Other10th Asian Conference on Computer Vision, ACCV 2010
CountryNew Zealand
CityQueenstown
Period11/8/1011/12/10

Fingerprint

Reflectional symmetry
Feature Point
Pruning
Outlier
Symmetry
Path
Experimental Results
Sampling
Demonstrate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Liu, J., & Liu, Y. (2011). Curved reflection symmetry detection with self-validation. In Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers (PART 4 ed., pp. 102-114). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6495 LNCS, No. PART 4). https://doi.org/10.1007/978-3-642-19282-1_9
Liu, Jingchen ; Liu, Yanxi. / Curved reflection symmetry detection with self-validation. Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers. PART 4. ed. 2011. pp. 102-114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4).
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Liu, J & Liu, Y 2011, Curved reflection symmetry detection with self-validation. in Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers. PART 4 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 4, vol. 6495 LNCS, pp. 102-114, 10th Asian Conference on Computer Vision, ACCV 2010, Queenstown, New Zealand, 11/8/10. https://doi.org/10.1007/978-3-642-19282-1_9

Curved reflection symmetry detection with self-validation. / Liu, Jingchen; Liu, Yanxi.

Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers. PART 4. ed. 2011. p. 102-114 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6495 LNCS, No. PART 4).

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

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AB - 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.

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Liu J, Liu Y. Curved reflection symmetry detection with self-validation. In Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers. PART 4 ed. 2011. p. 102-114. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4). https://doi.org/10.1007/978-3-642-19282-1_9