Skip to main content

Computational Symmetry

  • Living reference work entry
  • Latest version View entry history
  • First Online:
Computer Vision
  • 166 Accesses

Synonyms

Symmetry detection; Symmetry-based X

Footnote 1

Related Concepts

Definition

Symmetry is a mathematical concept as well as a widely used word for describing observed patterns. Formally:

Definition 1

Let S be a proper subset of Rn. Then an isometry (a distance preserving mapping) g is a symmetry of S if and only if g(S) = S.

Computational symmetry is a branch of research using computers to model, analyze, synthesize, and manipulate symmetries in digital form [41].

Background

Symmetry is a pervasive phenomenon presenting itself in all forms and scales, from galaxies to microscopic biological structures, in nature and man-made environments. Much of one’s understanding of the world is based on the perception and recognition of recurring patterns that are generalized by the mathematical concept of symmetries [85, 14, 12]. Humans and animals have an innate ability to perceive and take advantage of symmetry in everyday life [80, 22, 73, 84], while harnessing this...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Notes

  1. 1.

    Electronic Supplementary Material: The online version of this article (https://doi.org/10.1007/978-3-030-03243-2_640-2) contains supplementary material, which is available to authorized users.

References

  1. Arnheim R (2004) Art and visual perception: a psychology of the creative eye. University of California Press, Berkelay

    Google Scholar 

  2. Begelfor E, Werman M (2005) How to put probabilities on homographies. IEEE Trans Pattern Anal Mach Intell 27(10):1666–1670

    Article  Google Scholar 

  3. Bieberbach L (1910) Über die Bewegungsgruppen der n-dimensional en Euklidischen Räume mit einem endlichen Fundamental bereich. Göttinger Nachrichten, pp 75–84

    Google Scholar 

  4. Biederman I (1985) Human image understanding: recent research and a theory. Comput Vis Graph Image Process 32:29–73

    Article  Google Scholar 

  5. Birkoff GD (1932) Aesthetic measure. Harvard University Press, Cambridge, MA

    Google Scholar 

  6. Blum H (1973) Biological shape and visual science (part I). J Theor Biol 38:205–287

    Article  Google Scholar 

  7. Blumenthal AF, Davis LS, Rosenfeld A (1977) Detecting natural “plateaus” in one-dimensional patterns. IEEE Trans Comput 26(2):178–179

    Article  Google Scholar 

  8. Brady M, Asada H (1984) Smoothed local symmetries and their implementation. Int J Robot Res 3(3):36–61

    Article  Google Scholar 

  9. Chastain E, Liu Y (2007) Quantified symmetry for entorhinal spatial maps. Neurocomputing 70(10–12):1723–1727

    Article  Google Scholar 

  10. Chen P, Hays JH, Lee S, Park M, Liu Y (2007) A quantitative evaluation of symmetry detection algorithms. Technical Report PSU-CSE-07011 (also listed as Technical report CMU-RI-TR-07-36), The Pennsylvania State University, State College

    Google Scholar 

  11. Cohen A, Oswald MR, Liu Y, Pollefeys M (2017) Symmetry-aware facade parsing with occlusions. In: 2017 international conference on 3D vision (3DV). IEEE, pp 393–401

    Google Scholar 

  12. Conway JH, Burgiel H, Goodman-Strauss C (2008) The symmetries of things. A K Peters, Wellesley

    MATH  Google Scholar 

  13. Coxeter HSM Introduction to geometry, 2nd edn. Wiley, New York (1980)

    Google Scholar 

  14. Coxeter HSM, Moser WOJ (1980) Generators and relations for discrete groups, 4th edn. Springer, New York

    Book  MATH  Google Scholar 

  15. Davis LS (1977) Understanding shape: angles and sides. IEEE Trans Comput 26(3):236–242

    Article  MATH  Google Scholar 

  16. Fedorov ES (1885) The elements of the study of figures. [Russian] (2) 21. In: Zapiski Imperatorskogo S. Peterburgskogo Mineralogichesgo Obshchestva [Proc S. Peterb Mineral Soc], pp 1–289

    Google Scholar 

  17. Fedorov ES (1891) Symmetry in the plane. [russian] (2) 28. In: Zapiski Imperatorskogo S. Peterburgskogo Mineralogichesgo Obshchestva [Proc S. Peterb Mineral Soc], pp 345–390

    Google Scholar 

  18. Fedorov ES (1891) Symmetry of finite figures. [russian] (2) 28. In: Zapiski Imperatorskogo S. Peterburgskogo Mineralogichesgo Obshchestva [Proc S. Peterb Mineral Soc], pp 1–146

    Google Scholar 

  19. Funk C, Lee S, Oswald M, Tsogkas S, Shen W, Cohen A, Dickinson S, Liu Y (2017) 2017 iccv challenge: detecting symmetry in the wild. In: 2017 IEEE international conference on computer vision workshops (ICCVW), pp 1692–1701

    Google Scholar 

  20. Funk C, Liu Y (2016) Symmetry reCAPTCHA. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5165–5174

    Google Scholar 

  21. Funk C, Liu Y (2017) Beyond planar symmetry: modeling human perception of reflection and rotation symmetries in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 793–803

    Google Scholar 

  22. Giurfa M, Eichmann B, Menzel R (1996) Symmetry perception in an insect. Nature 382(6590):458–461

    Article  Google Scholar 

  23. Greenberg MJ (1993) Euclidean and non-Euclidean geometries: development and history, 3rd edn. W. H. Freeman and Company, Dallas

    MATH  Google Scholar 

  24. Grünbaum B, Shephard GC (1987) Tilings and patterns. W.H. Freeman and Company, New York

    MATH  Google Scholar 

  25. Han J, McKenna SJ, Wang R (2008) Regular texture analysis as statistical model selection. In: ECCV08

    Google Scholar 

  26. Hays J, Leordeanu M, Efros A, Liu Y (2006) Discovering texture regularity as a higher-order correspondence problem. In: European conference on computer vision (ECCV’06)

    Google Scholar 

  27. Henry NFM, Lonsdale K (eds) (1969) International tables for X-ray crystallography, Volume 1, symmetry groups. The international union of crystallography. The Kynoch Press, Birmingham

    Google Scholar 

  28. Hong W, Yang AY, Ma Y (2004) On symmetry and multiple view geometry: structure, pose and calibration from a single image. Int J Comput Vis 60(3):241–265

    Article  Google Scholar 

  29. Kanade T (1981) Recovery of the 3-dimensional shape of an object from a single view. Artif Intell 17:75–116

    Article  Google Scholar 

  30. Korah T, Rasmussen D (2008) Analysis of building textures for reconstructing partially occluded facades. In: ECCV08, pp 359–372

    Google Scholar 

  31. Lee S, Liu Y (2010) Skewed rotation symmetry group detection. IEEE Trans Pattern Anal Mach Intell PAMI 32(9):1659–1672

    Article  Google Scholar 

  32. Lee S, Liu Y (2009) Skewed rotation symmetry group detection. IEEE Trans Pattern Anal Mach Intell 32(9):1659–1672

    Google Scholar 

  33. Lee S, Liu Y (2011) Curved glide-reflection symmetry detection. IEEE Trans Pattern Anal Mach Intell 34(2):266–278

    Google Scholar 

  34. Levinshtein A, Sminchisescu C, Dickinson S (2009) Multiscale symmetric part detection and grouping. In: ICCV

    Book  Google Scholar 

  35. Lin WC, Liu Y (2006) Tracking dynamic near-regular textures under occlusion and rapid movements. In: 9th European conference on computer vision (ECCV’06), vol 2, pp 44–55

    Google Scholar 

  36. Lin WC, Liu Y (2007) A lattice-based mrf model for dynamic near-regular texture tracking. IEEE Trans Pattern Anal Mach Intell 29(5):777–792

    Article  Google Scholar 

  37. Liu J, Liu Y (2010) Multi-target tracking of time-varying spatial patterns. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’10). IEEE Computer Society Press, pp 1–8

    Google Scholar 

  38. Liu J, Liu Y (2013) Grasp recurring patterns from a single view. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2003–2010

    Google Scholar 

  39. Liu J, Liu Y (2014) Local regularity-driven city-scale facade detection from aerial images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3778–3785

    Google Scholar 

  40. Liu Y (1990) Symmetry groups in robotic assembly planning. PhD thesis, University of Massachusetts, Amherst

    Google Scholar 

  41. Liu Y (2002) Computational symmetry. In: Hargittai I, Laurent TC (eds) Symmetry 2000, vol 80, chapter 21. Wenner-Gren international series. Portland, London, pp 231–245. ISBN I-85578-149-2

    Google Scholar 

  42. Liu Y, Belkina T, Hays H, Lublinerman R (2008) Image de-fencing. In: IEEE computer vision and pattern recognition (CVPR 2008), pp 1–8

    Google Scholar 

  43. Liu Y, Collins RT (2000) A computational model for repeated pattern perception using frieze and wallpaper groups. In: Computer vision and pattern recognition conference (CVPR’00), Hilton Head. IEEE Computer Society Press, pp 537–544. http://www.ri.cmu.edu/pubs/pub_3302.html

    Google Scholar 

  44. Liu Y, Collins RT (2001) Skewed symmetry groups. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’01), Kauai. IEEE Computer Society Press, pp 872–879. http://www.ri.cmu.edu/pubs/pub_3815.html

    Google Scholar 

  45. Liu Y, Collins RT, Rothfus WE (2001) Robust midsagittal plane extraction from normal and pathological 3D neuroradiology images. IEEE Trans Med Imaging 20(3):175–192

    Article  Google Scholar 

  46. Liu Y, Collins RT, Tsin Y (2002) Gait sequence analysis using frieze patterns. In: Proceedings of the 7th European conference on computer vision (ECCV’02), A longer version can be found as CMU RI tech report 01-38 (2001), Copenhagen

    Google Scholar 

  47. Liu Y, Collins RT, Tsin Y (2004) A computational model for periodic pattern perception based on frieze and wallpaper groups. IEEE Trans Pattern Anal Mach Intell 26(3):354–371

    Article  Google Scholar 

  48. Liu Y, Dellaert F (1998) A classification-based similarity metric for 3D image retrieval. In: Proceedings of computer vision and pattern recognition conference (CVPR’98), Santa Barbara. IEEE Computer Society Press, pp 800–807

    Google Scholar 

  49. Liu Y, Hel-Or H, Kaplan CS, Van Gool L (2010) Computational symmetry in computer vision and computer graphics: a survey. Found Trends(r) Comput Graph Vis 5(1/2):1–165

    MATH  Google Scholar 

  50. Liu Y, Lin WC (2003) Deformable texture: the irregular-regular-irregular cycle. In: Texture 2003, The 3rd international workshop on texture analysis and synthesis, Nice, pp 65–70

    Google Scholar 

  51. Liu Y, Lin WC, Hays J (2004) Near-regular texture analysis and manipulation. ACM Trans Graph (SIGGRAPH) 23(3):368–376

    Article  Google Scholar 

  52. Liu Y, Palmer J (2003) A quantified study of facial asymmetry in 3D faces. In: IEEE international workshop on analysis and modeling of faces and gestures. IEEE, pp 222–229

    Google Scholar 

  53. Liu Y, Schmidt K, Cohn J, Mitra S (2003) Facial asymmetry quantification for expression invariant human identification. Comput Vis Image Underst J 91(1/2):138–159. Special issue on face recognition, Martinez, Yang and Kriegman (eds)

    Google Scholar 

  54. Liu Y, Schmidt K, Cohn J, Weaver RL (2002) Facial asymmetry quantification for expression invariant human identification. In: International conference on automatic face and gesture recognition (FG’02)

    Google Scholar 

  55. Liu Y, Teverovskiy L, Carmichael O, Kikinis R, Shenton M, Carter CS, Stenger VA, Davis S, Aizenstein H, Becker J, Lopez O, Meltzer C (2004) Discriminative MR image feature analysis for automatic schizophrenia and alzheimer’s disease classification. In: 7th international conference on medical imaging computing and comptuer assisted intervention (MICCAI 2004). Springer, pp 378–385

    Google Scholar 

  56. Liu Y, Teverovskiy L, Lopez O, Aizenstein H, Becker J, Meltzer C (2007) Discovery of “biomarkers” for Alzheimer’s disease prediction from structural MR images. In: 2002 IEEE international symposium on biomedical imaging: macro to nano, pp 1344–1347

    Google Scholar 

  57. Liu Y, Tsin Y (2002) The promise and perils of near-regular texture. In: Texture 2002, Copenhagen, pp 657–671. In conjuction with ECCV’02

    Google Scholar 

  58. Liu Y, Tsin Y, Lin W (2005) The promise and perils of near-regular texture. Int J Comput Vis 62(1–2):145159

    Google Scholar 

  59. Liu Y, Dellaert F, Rothfus WE, Moore A, Schneider J, Kanade T (2001) Classification-driven pathological neuroimage retrieval using statistical asymmetry measures. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 655–665

    MATH  Google Scholar 

  60. Lowe DG (1985) Perceptual organization and visual recognition. Kluwer Academic, New York

    Book  Google Scholar 

  61. Makadia A, Daniilidis K (2006) Rotation recovery from spherical images without correspondences. 28:1170–1175

    Google Scholar 

  62. Milnor J (1976) Hilbert’s problem 18. In: Proceedings of symposia in pure mathematics, vol 28. American Mathematical Society, ISBN 0-8218-1428-1 (Browder, Felix E., Mathematical developments arising from Hilbert problems)

    Google Scholar 

  63. Mitra S, Lazar N, Liu Y (2007) Understanding the role of facial asymmetry in human face identification. Stat Comput 17:57–70

    Article  MathSciNet  Google Scholar 

  64. Mitra S, Liu Y (2004) Local facial asymmetry for expression classification. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’04), Washington, DC. IEEE Computer Society Press, pp 889–894. http://www.ri.cmu.edu/pubs/pub_4640.html

    Google Scholar 

  65. Nevatia R, Binford TO (1977) Description and recognition of curved objects. Artif Intell 8(1):77–98

    Article  MATH  Google Scholar 

  66. Park M, Brocklehurst K, Collins RT, Liu Y (2010) Image de-fencing revisited. In: Asian conference on computer vision (ACCV’10). IEEE Computer Society Press, pp 1–13

    Google Scholar 

  67. Park M, Brocklehurst K, Collins RT, Liu Y (2010) Translation-symmetry-based perceptual grouping with applications to urban scenes. In: Asian conference on computer vision (ACCV’10). IEEE Computer Society Press, pp 1–14

    Google Scholar 

  68. Park M, Brocklehurst K, Collins RT, Liu Y (2009) Deformed lattice detection in real-world images using mean-shift belief propagation. IEEE Trans Pattern Anal Mach Intell PAMI 31(10)

    Google Scholar 

  69. Park M, Lee S, Chen P, Kashyap S, Butt AA, Liu Y (2008) Performance evaluation of state-of-the-art discrete symmetry detection algorithms. In: IEEE conference on computer vision and pattern recognition (CVPR 2008), pp 1–8

    Google Scholar 

  70. Park M, Liu Y, Collins RT (2008) Deformed lattice detection via mean-shift belief propagation. In: Proceedings of the 10th European conference on computer vision (ECCV’08)

    Google Scholar 

  71. Park M, Liu Y, Collins RT (2008) Efficient mean shift belief propagation for vision tracking. In: Proceedings of computer vision and pattern recognition conference (CVPR’08). IEEE Computer Society Press

    Google Scholar 

  72. Pentland AP (1986) Perceptual organization and the representation of natural form. Artif Intell 28:293–331

    Article  MathSciNet  Google Scholar 

  73. Rodríguez I, Gumbert A, Hempel de Ibarra N, Kunze J, Giurfa M (2004) Symmetry is in the eye of the ‘beeholder’: innate preference for bilateral symmetry in flower-naïve bumblebees. Naturwissenschaften 91(8):374–377

    Article  Google Scholar 

  74. Schattschneider D (1978) The plane symmetry groups: their recognition and notation. Am Math Mon 85:439–450

    Article  MathSciNet  MATH  Google Scholar 

  75. Schindler G, Krishnamurthy P, Lublinerman R, Liu Y, Dellaert F (2008) Detecting and matching repeated patterns for automatic geo-tagging in urban environments. In: IEEE computer vision and pattern recognition (CVPR 2008), pp 1–8

    Google Scholar 

  76. Seitz SM, Dyer CR (1996) View morphing. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques, pp 21–30

    Google Scholar 

  77. Sun Y, Bhanu B (2009) Symmetry integrated region-based image segmentation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’08). IEEE Computer Society Press, pp 826–831

    Google Scholar 

  78. Terzopoulos D, Witkin A, Kass M (1987) Symmetry-seeking models and 3D object reconstruction. Int J Comput Vis 1:211–221

    Article  Google Scholar 

  79. Teverovskiy L, Becker J, Lopez O, Liu Y (2008) Quantified brain asymmetry for age estimation of normal and AD/MCI subjects. In: 2008 IEEE international symposium on biomedical imaging: nano to macro, pp 1509–1512

    Google Scholar 

  80. Thompson DW (1961) On growth and form. Cambridge University Press, Cambridge, England

    Google Scholar 

  81. Tsin Y, Liu Y, Ramesh V (2001) Texture replacement in real images. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR’01), Kauai. IEEE Computer Society Press, pp 539–544

    Google Scholar 

  82. Tuytelaars T, Turina A, Van Gool L (2003) Non-combinatorial detection of regular repetitions under perspective skew. IEEE Trans Pattern Anal Mach Intell PAMI 25(4):418–432

    Article  Google Scholar 

  83. Tuzel O, Subbarao R, Meer P (2005) Simultaneous multiple 3D motion estimation via mode finding on lie groups. In: Proceedings of the 10th IEEE international conference on computer vision (ICCV’05), vol I, pp 18–25

    Google Scholar 

  84. Tyler CW (ed) (1996) Human symmetry perception and its computational analysis. VSP, Utrecht, pp 1804–1816

    MATH  Google Scholar 

  85. Weyl H (1952) Symmetry. Princeton University Press, Princeton

    Book  MATH  Google Scholar 

  86. Wolff M, Collins RT, Liu Y (2016) Regularity-driven facade matching between aerial and street views. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1591–1600

    Google Scholar 

  87. Changchang Wu, Frahm J-M, Pollefeys M (2010) Detecting large repetitive structures with salient boundaries. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision ECCV 2010. Lecture notes in computer science, vol 6312. Springer, Berlin/Heidelberg, pp 142–155. https://doi.org/10.1007/978-3-642-15552-9_11

    Chapter  Google Scholar 

  88. Shangzhe Wu, Rupprecht C, Vedaldi A (2020) Unsupervised learning of probably symmetric deformable 3D objects from images in the wild. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1–10

    Google Scholar 

  89. Yang AY, Rao S, Huang K, Hong W, Ma Y (2003) Geometric segmentation of perspective images based on symmetry groups. In: Proceedings of the 10th IEEE international conference on computer vision (ICCV’03), vol 2, p 1251

    Google Scholar 

  90. Yu C-P, Ruppert G, Collins R, Nguyen D, Falcao A, Liu Y (2014) 3D blob based brain tumor detection and segmentation in MR images. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, pp 1192–1197

    Google Scholar 

  91. Zabrodsky H, Avnir D Measuring symmetry in structural chemistry. In: Hargittai I (ed) Advanced molecular structure research, vol 1. JAI Press, Greenwich (1993)

    Google Scholar 

  92. Zabrodsky H, Avnir D (1995) Continuous symmetry measures, IV: chirality. J Am Chem Soc 117:462–473

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Liu, Y. (2021). Computational Symmetry. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_640-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03243-2_640-2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Computational Symmetry
    Published:
    19 June 2021

    DOI: https://doi.org/10.1007/978-3-030-03243-2_640-2

  2. Original

    Computational Symmetry
    Published:
    24 February 2021

    DOI: https://doi.org/10.1007/978-3-030-03243-2_640-1