Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms: Algorithm and Results

Citation

Elawady M, Ducottet C, Alata O, Barat C & Colantoni P (2017) Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms: Algorithm and Results. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), Venice, Italy, 22.10.2017-29.10.2017. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/iccvw.2017.203

Abstract
The proposed algorithm detects globally the symmetry axes inside an image plane. The main steps are as follows: We firstly extract edge features using Log-Gabor filters with different scales and orientations. Afterwards, we use the edge characteristics associated with the textural and color information as symmetrical weights for voting triangulation. In the end, we construct a polar-based voting histogram based on the accumulation of the symmetry contribution (local texture and color information), in order to find the maximum peaks presenting as candidates of the primary symmetry axes.

Keywords
Image color analysis; Histograms; Image edge detection; Conferences; Gray-scale; Measurement; Feature extraction

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