Title :
Curved Glide-Reflection Symmetry Detection
Author :
Lee, Seungkyu ; Liu, Yanxi
Author_Institution :
Adv. Media Lab., Samsung Adv. Inst. of Technol. (SALT), Yongin, South Korea
Abstract :
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.
Keywords :
computer vision; feature extraction; image segmentation; 2D euclidean space; APS; axis parameter space; bilateral reflection symmetry; biomedical imaging; computer vision; curved glide reflection symmetry detection; feature based approach; saliency detection; unsegmented 2D images; Algorithm design and analysis; Computer vision; Detection algorithms; Feature extraction; Image edge detection; Three dimensional displays; Symmetry; curved axis; curved surface.; glide reflection; Algorithms; Animals; Databases, Factual; Humans; Image Processing, Computer-Assisted; Pattern Recognition, Automated; Tomography, X-Ray Computed;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2011.118