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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ICCV 2017, IEEE International Conference on Computer Vision Workshop Detecting Symmetry in the Wild, Oct 2017, Venice, Italy. IEEE International Conference on Computer Vision Workshop Detecting Symmetry in the Wild. 〈10.1109/ICCVW.2017.202〉
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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. IEEE International Conference on Computer Vision Workshop Detecting Symmetry in the Wild. 〈10.1109/ICCVW.2017.202〉. 〈ujm-01637175〉

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