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.
Cite
Text
Elawady et al. "Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.202Markdown
[Elawady et al. "Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/elawady2017iccvw-waveletbased/) doi:10.1109/ICCVW.2017.202BibTeX
@inproceedings{elawady2017iccvw-waveletbased,
title = {{Wavelet-Based Reflection Symmetry Detection via Textural and Color Histograms}},
author = {Elawady, Mohamed and Ducottet, Christophe and Alata, Olivier and Barat, Cécile and Colantoni, Philippe},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {1725-1733},
doi = {10.1109/ICCVW.2017.202},
url = {https://mlanthology.org/iccvw/2017/elawady2017iccvw-waveletbased/}
}