We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.
@article{arxiv.1609.05257,
title = {A convolutional approach to reflection symmetry},
author = {Marcelo Cicconet and Vighnesh Birodkar and Mads Lund and Michael Werman and Davi Geiger},
journal= {arXiv preprint arXiv:1609.05257},
year = {2016}
}
Comments
This paper is under consideration at Pattern Recognition Letters