Title :
Curved glide-reflection symmetry detection
Author :
Seungkyu Lee ; Yanxi Liu
Author_Institution :
Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
We generalize reflection symmetry detection to a curved glide reflection symmetry detection problem. We propose a unifying, local feature based approach for curved glide reflection symmetry detection from real, unsegmented images, where the classic reflection symmetry becomes one of four special cases. Our method detects and groups statistically dominant local reflection axes in a 3D parameter space. A curved glid reflection symmetry axis is estimated by a set of contiguous local straight reflection axes. Experimental results of the proposed algorithm on 40 real world images demonstrate promising performance.
Keywords :
axial symmetry; curve fitting; feature extraction; 3D parameter space; curved glide detction; image detection; reflection axes; reflection symmetry detection; Application software; Computer science; Computer vision; Detection algorithms; Face detection; Image analysis; Object detection; Pattern recognition; Reflection; Vehicle detection;
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-3992-8
DOI :
10.1109/CVPR.2009.5206814