Robust detection of skewed symmetries by combining local and semi-local affine invariants

Dinggang Shen, Horace H.S. Ip, Eam Khwang Teoh

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Affine-invariant feature vector (Ip and Shen Image Vision Comput. 16 (2) (1998) 135-146), that captures both local and semi-local geometric features around each point of the object boundary is applied here for the detection of skewed symmetries. Based on the affine-invariant shape representation, the problem of detecting symmetry axes has been formulated as a problem of detecting lines, with known orientations, in a local similarity matrix of an object. Since the feature vector extracts sufficient local and semi-local shape information for every point along the object boundary, the process of checking symmetric point pairs is thus robust against both noises and deformations. Moreover, our technique is able to detect all the local reflectional symmetries contained in the object. Various experimental results have shown the robustness and effectiveness of our method in detecting skewed symmetries from both self-symmetric objects and generalized objects.

Original languageEnglish
Pages (from-to)1417-1428
Number of pages12
JournalPattern Recognition
Volume34
Issue number7
DOIs
Publication statusPublished - 2001 Jul
Externally publishedYes

Keywords

  • Local invariants
  • Reflectional symmetry
  • Rotational symmetry
  • Semi-local invariants
  • Skewed symmetry

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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