Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation - Université Jean-Monnet-Saint-Étienne Access content directly
Conference Papers Year : 2017

Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation

Christophe Ducottet
Mohamed Elawady
Olivier Alata
Cecile Barat
  • Function : Author
  • PersonId : 844844

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.
Fichier principal
Vignette du fichier
caip2017-multiple-reflection-final.pdf (2.37 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

ujm-01637159 , version 1 (17-11-2017)

Identifiers

Cite

Christophe Ducottet, Mohamed Elawady, Olivier Alata, Cecile Barat, Philippe Colantoni. Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation. CAIP 2017, 17th International Conference on Computer Analysis of Images and Patterns, Aug 2017, Ystad, Sweden. pp.344-355, ⟨10.1007/978-3-319-64689-3_28⟩. ⟨ujm-01637159⟩
78 View
313 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More