Abstract
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. We release code and a new, larger image database.
Original language | English |
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Journal | Pattern Recognition Letters |
Volume | 95 |
Pages (from-to) | 44-50 |
Number of pages | 7 |
ISSN | 0167-8655 |
DOIs | |
Publication status | Published - 1 Aug 2017 |
Keywords
- Mirror symmetry
- Reflection symmetry