Abstract
Symmetry and self-similarity are the cornerstone of Nature, exhibiting themselves through the shapes of natural creations and ubiquitous laws of physics. Since many natural objects are symmetric, the absence of symmetry can often be an indication of some anomaly or abnormal behavior. Therefore, detection of asymmetries is important in numerous practical applications, including crystallography, medical imaging, and face recognition, to mention a few. Conversely, the assumption of underlying shape symmetry can facilitate solutions to many problems in shape reconstruction and analysis. Traditionally, symmetries are described as extrinsic geometric properties of the shape. While being adequate for rigid shapes, such a description is inappropriate for non-rigid ones: extrinsic symmetry can be broken as a result of shape deformations, while its intrinsic symmetry is preserved. In this paper, we present a generalization of symmetries for non-rigid shapes and a numerical framework for their analysis, addressing the problems of full and partial exact and approximate symmetry detection and classification.
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Alt, H., Mehlhorn, K., Wagener, H., & Welzl, E. (1988). Congruence, similarity, and symmetries of geometric objects. Discrete & Computational Geometry, 3, 237–256.
Anguelov, D., Srinivasan, P., Pang, H. C., Koller, D., Thrun, S., & Davis, J. (2005). The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces. In Proc. NIPS, 2005.
Atallah, M. J. (1985). On symmetry detection. IEEE Transactions on Computers, c-34(7).
Borg, I., & Groenen, P. (1997). Modern multidimensional scaling—theory and applications. New York: Springer.
Bronstein, A. M., & Bronstein, M. M. (2008). Not only size matters: Regularized partial matching of nonrigid shapes. In Proc. non-rigid shape analysis and deformable image registration (NORDIA) workshop. Proc. of computer vision and pattern recognition (CVPR), June 2008.
Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2006). Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching. Proceedings of the National Academy of Sciences (PNAS), 103/5, 1168–1172.
Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2007). Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes. In Proc. international conference on computer vision (ICCV), 2007.
Bronstein, A., Bronstein, M., & Kimmel, R. (2008a). Numerical geometry of non-rigid shapes. Berlin: Springer.
Bronstein, A. M., Bronstein, M. M., Bruckstein, A. M., & Kimmel, R. (2008b). Analysis of two-dimensional non-rigid shapes. International Journal of Computer Vision, 78(1), 67–88.
Bronstein, A. M., Bronstein, M. M., Bruckstein, A. M., & Kimmel, R. (2009a). Partial similarity of objects, or how to compare a centaur to a horse. International Journal of Computer Vision, 84(2), 163–183.
Bronstein, A. M., Bronstein, M. M., Kimmel, R., Mahmoudi, M., & Sapiro, G. (2009b). A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching. International Journal of Computer Vision, to appear.
Cheung, K. T., & Ip, H. S. (1998). Symmetry detection using complex moments. In Proc. international conference on pattern recognition (ICPR) (Vol. 2, pp. 1473–1475).
Coifman, R. R., & Lafon, S. (2006). Diffusion maps. Applied and Computational Harmonic Analysis, 21(1), 5–30. Definition of diffusion distance.
Cornelius, H., & Loy, G. (2006). Detecting rotational symmetry under affine projection. In Proc. international conference on pattern recognition (ICPR) (Vol. 2, pp. 292–295).
De Natale, F. G. B., Giusto, D. D., & Maccioni, F. (1997). A symmetry-based approach to facial features extraction. In Proc. international conference on digital signal processing (ICDSP) (Vol. 2, pp. 521–525).
Derrode, S., & Ghorbel, F. (2004). Shape analysis and symmetry detection in gray-level objects using the analytical Fourier-Mellin representation. Signal Processing, 84(1), 25–39.
Elad, A., & Kimmel, R. (2003). On bending invariant signatures for surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 25(10), 1285–1295.
Gal, Ran, Shamir, Ariel, & Cohen-Or, Daniel (2007). Pose-oblivious shape signature. IEEE Transactions on Visualization and Computer Graphics, 13(2), 261–271.
Gelfand, N., Mitra, N. J., Guibas, L. J., & Pottmann, H. (2005). Robust global registration. In Proc. symposium on geometry processing (SGP) (pp. 197–206).
Gofman, Y., & Kiryati, N. (1996). Detecting symmetry in grey level images: The global optimization approach. In Proc. international conference on pattern recognition (ICPR) (pp. 951–956).
Hamza, A. B., & Krim, H. (2005). Probabilistic shape descriptor for triangulated surfaces. In Proc. IEEE international conf. image processing (ICIP) (Vol. 1, pp. 1041–1044).
Haraguchi, S., Takada, K., & Yasuda, Y. (2001). Facial asymmetry in subjects with skeletal class III deformity. Angle Orthodontist, 72(1), 28–35.
Hochbaum, D., & Shmoys, D. (1985). A best possible heuristic for the k-center problem. Mathematics of Operations Research, 10(2), 180–184.
Huisinga-Fischer, C. E., Souren, J. P., Werken, F. S. B., Prahl-Andersen, B., & van Ginkel, F. (2004). Perception of symmetry in the face. Journal of Craniofacial Surgery, 15(1), 128–134.
Kazhdan, M., Chazelle, B., Dobkin, D., Funkhouser, T., & Rusinkiewicz, S. (2003). A reflective symmetry descriptor for 3D models. Algorithmica, 38(1), 201–225.
Kepler, J. (1611). Strena seu de nive sexangula. Frankfurt.
Kimmel, R., & Sethian, J. A. (1998). Computing geodesic paths on manifolds. Proceedings of the National Academy of Sciences (PNAS), 95(15), 8431–8435.
Lasowski, R., Tevs, A., Seidel, H. P., & Wand, M. (2009). A probabilistic framework for partial intrinsic symmetries in geometric data. In Proc. of international conference on computer vision (ICCV).
Lévy, B. (2006). Laplace-Beltrami eigenfunctions towards an algorithm that “understands” geometry. In Int’l conf. shape modeling and applications, 2006.
Ling, H., & Jacobs, D. (2007). Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 29(2), 286–299.
Liu, R. F., Zhang, H., Shamir, A., & Cohen-Or, D. (2009) A part-aware surface metric for shape analysis. Computer Graphics Forum, 28, 397–406.
Liu, Y., Collins, R., & Tsin, Y. (2004). A computational model for periodic pattern perception based on frieze and wallpaper groups. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 26(3), 354–371.
Loy, G., & Eklundth, J. (2006). Detecting symmetry and symmetric constellations of features. In Proc. computer vision and pattern recognition (CVPR) (Vol. 2, pp. 508–521).
Mahmoudi, M., & Sapiro, G. (2009). Three-dimensional point cloud recognition via distributions of geometric distances. Graphical Models, 71(1), 22–31.
Mancas, M., Gosselin, B., & Macq, B. (2005). Fast and automatic tumoral area localisation using symmetry. In Proc. international conference on acoustics, speech and signal processing (ICASSP) (Vol. 2, pp. 725–728).
Marola, G. (1989). On the detection of axes of symmetry of symmetric and almost symmetric planner images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 11(1).
Mateus, D., Horaud, R., Knossow, D., & Boyer, E. (2008). Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration. In Proc. computer vision and pattern recognition (CVPR).
Mealey, L., Bridgstock, R., & Townsend, G. C. (1999). Symmetry and perceived facial attractiveness: a monozygotic co-twin comparison. Journal of Personality and Social Psychology, 76(1), 151–158.
Mémoli, F. (2008). Gromov-Hausdorff distances in Euclidean spaces. In Proc. non-rigid shape analysis and deformable image registration (NORDIA) workshop. Proc. of computer vision and pattern recognition (CVPR).
Mémoli, F., & Sapiro, G. (2005). A theoretical and computational framework for isometry invariant recognition of point cloud data. Foundations of Computational Mathematics, 5, 313–346.
Mitra, N. J., Guibas, L. J., & Pauly, M. (2006). Partial and approximate symmetry detection for 3D geometry. In Proc. international conference and exhibition on computer graphics and interactive techniques (SIGGRAPH) (pp. 560–568).
Moenning, C., & Dodgson, N. (2003). A new point cloud simplification algorithm. In Proc. international conference on visualization, imaging and image processing.
Mumford, D., & Shah, J. (1990). Boundary detection by minimizing functionals. In Proc. international conference on computer vision (ICCV).
Ovsjanikov, M., Sun, J., & Guibas, L. (2008). Global intrinsic symmetries of shapes. In Proc. eurographics symposium on geometry processing (SGP) (Vol. 27).
Peyré, G., & Cohen, L. (2006). Geodesic remeshing using front propagation. International Journal of Computer Vision (IJCV), 69(1), 145–156.
Pinkall, U., & Polthier, K. (1993). Computing discrete minimal surfaces and their conjugates. Experimental Mathematics, 2(1), 15–36.
Raviv, D., Bronstein, A. M., Bronstein, M. M., & Kimmel, R. (2007). Symmetries of non-rigid shapes. In Proc. non-rigid registration and tracking (NRTL) workshop. Proc. of international conference on computer vision (ICCV).
Reisfeld, D., & Yeshurun, Y. (1992). Robust detection of facial features by generalized symmetry. In Proc. international conference on pattern recognition (ICPR) (Vol. 1, pp. 117–120).
Reuter, M., Wolter, F.-E., & Peinecke, N. (2006). Laplace Beltrami spectra as shape-DNA of surfaces and solids. Computer-Aided Design, 38, 342–366.
Riklin-Raviv, T., Sochen, N., & Kiryati, N. (2009). On symmetry, perspectivity, and level-set-based segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 31(8), 1458–1471.
Rustamov, R. M. (2007). Laplace-Beltrami eigenfunctions for deformation invariant shape representation. In Proc. eurographics symposium on geometry processing (SGP) (pp. 225–233).
Shimshoni, I., Moses, Y., & Lindernbaum, M. (2000). Shape reconstruction of 3D bilaterally symmetric surfaces. International Journal of Computer Vision, 39(2), 97–110.
Sun, C., & Sherrah, J. (1997). 3D symmetry detection using the extended Gaussian image. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 19(2), 164–168.
Weyl, H. (1983). Symmetry. Princeton: Princeton University Press.
Wolter, J. D., Woo, T. C., & Volz, R. A. (1985). Optimal algorithms for symmetry detection in two and three dimensions. The Visual Computer, 1, 37–48.
Xu, K., Zhang, H., Tagliasacchi, A., Liu, L., Li, G., Meng, M., & Xiong, Y. (2009). Partial intrinsic reflectional symmetry of 3d shapes. In Proc. SIGGRAPH Asia.
Yang, X., Adluru, N., Latecki, L. J., Bai, X., & Pizlo, Z. (2008). Symmetry of shapes via self-similarity. In Proc. international symposium on advances in visual computing (pp. 561–570).
Zabrodsky, H., Peleg, S., & Avnir, D. (1995). Symmetry as a continuous feature. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 17(12), 1154–1166.
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Raviv, D., Bronstein, A.M., Bronstein, M.M. et al. Full and Partial Symmetries of Non-rigid Shapes. Int J Comput Vis 89, 18–39 (2010). https://doi.org/10.1007/s11263-010-0320-3
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DOI: https://doi.org/10.1007/s11263-010-0320-3