Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation
This work addresses symmetry detection for computer vision applications, offering an incremental improvement over prior methods.
The paper tackled the problem of detecting multiple reflection symmetries in images by proposing a weighted linear-directional kernel density estimation method, achieving superior performance on two public datasets compared to existing approaches.
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.