CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.02 - February
Issue No.02 - February (2012 vol.34)
Seungkyu Lee , Adv. Media Lab., Samsung Adv. Inst. of Technol. (SALT), Yongin, South Korea
Yanxi Liu , Dept. of Comput. Sci. & Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
We generalize the concept of bilateral reflection symmetry to curved glide-reflection symmetry in 2D euclidean space, such that classic reflection symmetry becomes one of its six special cases. We propose a local feature-based approach for curved glide-reflection symmetry detection from real, unsegmented 2D images. Furthermore, we apply curved glide-reflection axis detection for curved reflection surface detection in 3D images. Our method discovers, groups, and connects statistically dominant local glide-reflection axes in an Axis-Parameter-Space (APS) without preassumptions on the types of reflection symmetries. Quantitative evaluations and comparisons against state-of-the-art algorithms on a diverse 64-test-image set and 1,125 Swedish leaf-data images show a promising average detection rate of the proposed algorithm at 80 and 40 percent, respectively, and superior performance over existing reflection symmetry detection algorithms. Potential applications in computer vision, particularly biomedical imaging, include saliency detection from unsegmented images and quantification of deviations from normality. We make our 64-test-image set publicly available.
image segmentation, computer vision, feature extraction, saliency detection, curved glide reflection symmetry detection, bilateral reflection symmetry, 2D euclidean space, feature based approach, unsegmented 2D images, axis parameter space, APS, computer vision, biomedical imaging, Three dimensional displays, Detection algorithms, Feature extraction, Computer vision, Image edge detection, Algorithm design and analysis, curved surface., Symmetry, glide reflection, curved axis
Seungkyu Lee, Yanxi Liu, "Curved Glide-Reflection Symmetry Detection", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 2, pp. 266-278, February 2012, doi:10.1109/TPAMI.2011.118