Curved Reflection Symmetry Detection with Self-validation

13 years 22 days ago
Curved Reflection Symmetry Detection with Self-validation
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.
Jingchen Liu, Yanxi Liu
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where ACCV
Authors Jingchen Liu, Yanxi Liu
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