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.
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CAIP 2017, 17th International Conference on Computer Analysis of Images and Patterns, Aug 2017, Ystad, Sweden. 10424, pp.344-355, 2017, Lecture Notes in Computer Science. 〈10.1007/978-3-319-64689-3_28〉
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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. 10424, pp.344-355, 2017, Lecture Notes in Computer Science. 〈10.1007/978-3-319-64689-3_28〉. 〈ujm-01637159〉

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