@inproceedings{07e73023a2814ed5b47489cc4a9dfaae,
title = "Multiple reflection symmetry detection via linear-directional kernel density estimation",
abstract = "Symmetry is an important composition feature by investigating similar sides inside an image plane. It has a crucial effect to recognize man-made or nature objects within the universe. Recent symmetry detection approaches used a smoothing kernel over different voting maps in the polar coordinate system to detect symmetry peaks, which split the regions of symmetry axis candidates in inefficient way. We propose a reliable voting representation based on weighted linear-directional kernel density estimation, to detect multiple symmetries over challenging real-world and synthetic images. Experimental evaluation on two public datasets demonstrates the superior performance of the proposed algorithm to detect global symmetry axes respect to the major image shapes.",
keywords = "kernel density estimation, linear-directional data, multiple symmetry, reflection symmetry, symmetry detection",
author = "Mohamed Elawady and Olivier Alata and Christophe Ducottet and C{\'e}cile Barat and Philippe Colantoni",
year = "2017",
month = jul,
day = "28",
doi = "10.1007/978-3-319-64689-3_28",
language = "English",
isbn = "9783319646886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "344--355",
editor = "Michael Felsberg and Anders Heyden and Norbert Kr{\"u}ger",
booktitle = "Computer Analysis of Images and Patterns",
note = "17th International Conference on Computer Analysis of Images and Patterns, CAIP 2017 ; Conference date: 22-08-2017 Through 24-08-2017",
}