Unified detection of skewed rotation, reflection and translation symmetries from affine invariant contour features

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

7 Scopus Citations
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Original languageEnglish
Pages (from-to)1764-1776
Journal / PublicationPattern Recognition
Issue number4
Publication statusPublished - Apr 2014


Symmetry detection is significant for object detection and recognition since symmetries are salient cues for distinguishing geometrical structures from cluttered backgrounds. This paper proposes a unified framework to detect rotation, reflection and translation symmetries simultaneously on unsegmented real-world images. The detection is performed based on affine invariant contour features, and is applicable under skewed imaging with distortions. Contours on a natural image are first matched to each other to find affine invariant matching pairs, which are then classified into three groups using a sign change criterion corresponding to the three types of symmetries. The three groups are used to vote for the corresponding symmetries: the voting for rotation utilizes a scheme of short line segments; the voting for reflection is based on a parameterization of axis equation, and the voting for translation uses a cascade-like approach. Experimental results of real-world images are provided with quantitative evaluations, validating that the proposed framework achieves desired performance. © 2013 Elsevier Ltd.

Research Area(s)

  • Affine invariance, Contour matching, Reflection symmetry, Rotation symmetry, Symmetry detection, Translation symmetry, Voting