Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation

Citation

Elawady M, Alata O, Ducottet C, Barat C & Colantoni P (2017) Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation. In: Krüger N, Heyden A & Felsberg M (eds.) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science, 10424. CAIP 2017: International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden, 22.08.2017-24.08.2017. Cham, Switzerland: Springer International Publishing, pp. 344-355. https://doi.org/10.1007/978-3-319-64689-3_28

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
Multiple symmetry; Symmetry detection; Reflection symmetry; Kernel density estimation; Linear-directional data

• Accepted manuscript