Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms

Abstract : In recent years, there has been renewed interest in bilateral symmetry detection in images. It consists in detecting the main bilateral symmetry axis inside artificial or natural images. State-of-the-art methods combine feature point detection, pairwise comparison and voting in Hough-like space. In spite of their good performance, they fail to give reliable results over challenging real-world and artistic images. In this paper, we propose a novel symmetry detection method using multi-scale edge features combined with local orientation histograms. An experimental evaluation is conducted on public datasets plus a new aesthetic-oriented dataset. The results show that our approach outperforms all other concurrent methods.
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Mohamed Elawady, Cécile Barat, Christophe Ducottet, Philippe Colantoni, Cecile Barat. Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms. 17th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2016) , Oct 2016, Lecce, Italy. pp.14 - 24, ⟨10.1007/978-3-319-48680-2_2⟩. ⟨ujm-01387193v2⟩

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