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.
Complete list of metadatas

Cited literature [40 references]  Display  Hide  Download

https://hal-ujm.archives-ouvertes.fr/ujm-01637159
Contributor : Christophe Ducottet <>
Submitted on : Friday, November 17, 2017 - 1:39:21 PM
Last modification on : Sunday, July 28, 2019 - 6:04:02 PM
Long-term archiving on : Sunday, February 18, 2018 - 3:13:46 PM

File

caip2017-multiple-reflection-f...
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

132

Files downloads

329