Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms

Abstract : Symmetry is one of the significant visual properties inside an image plane, to identify the geometrically balanced structures through real-world objects. Existing symmetry detection methods rely on descriptors of the local image features and their neighborhood behavior, resulting incomplete symmetrical axis candidates to discover the mirror similarities on a global scale. In this paper, we propose a new reflection symmetry detection scheme, based on a reliable edge-based feature extraction using Log-Gabor filters , plus an efficient voting scheme parameterized by their corresponding textural and color neighborhood information. Experimental evaluation on four single-case and three multiple-case symmetry detection datasets validates the superior achievement of the proposed work to find global symmetries inside an image.
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Mohamed Elawady, Christophe Ducottet, Olivier Alata, Cecile Barat, Philippe Colantoni. Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms. ICCV 2017, IEEE International Conference on Computer Vision Workshop Detecting Symmetry in the Wild, Oct 2017, Venice, Italy. ⟨10.1109/ICCVW.2017.202⟩. ⟨ujm-01637175⟩

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