Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms

Citation

Elawady M, Ducottet C, Alata O, Barat C & Colantoni P (2017) Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), Venice, 22.10.2017-29.10.2017. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/iccvw.2017.202

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.

Keywords
Feature extraction; Image edge detection; Image color analysis; Histograms; Frequency-domain analysis; Color

• Accepted manuscript