Lattice Gestalten

  • Eckart MichaelsenEmail author
  • Jochen Meidow
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


A separate operation is constructed for the aggregation of lattices of Gestalten. The lattice operation aggregates comparably many Gestalten in one step into the construction at much larger scale. This requires also search strategies that differ from the ones presented for friezes or rotational mandalas. As an example the perceptual grouping of permanent sactterers in synthetic aperture radar images is presented. Like in some other chapters of this book, the last section treats the operation under projective distortions.


  1. 1.
    Mitra NJ, Pauly M, Wand M, Ceylan D (2013) Symmetry in 3D geometry: extraction and applications. Comput Graph Forum 32(6):1–23CrossRefGoogle Scholar
  2. 2.
    Liu J, Slota G, Zheng G, Wu Z, Park M, Lee S, Rauschert I, Liu Y (2013) Symmetry detection from realworld images competition 2013: summary and results. In: CVPR 2013, workshopsGoogle Scholar
  3. 3.
    Korč F, Förstner W (2009) eTRIMS image database for interpreting images of man-made scenes. Technical report TR-IGG-P-2009-01, Department of Photogrammetry, University of Bonn. Accessed Aug 2018
  4. 4.
    Wenzel S (2016) High-level facade image interpretation using marked point processes. PhD thesis, Department of Photogrammetry, University of BonnGoogle Scholar
  5. 5.
    Tyleček R (2016) Probabilistic models for symmetric object detection in images. PhD thesis, Czech Technical University in PragueGoogle Scholar
  6. 6.
    Schack L, Soergel U (2014) Exploiting regular patterns to group persistent scatterers in urban areas. IEEE-JSTARS 7(1):4177–4183CrossRefGoogle Scholar
  7. 7.
    Desolneux A, Moisan L, Morel J-M (2008) From gestalt theory to image analysis: a probabilistic approach. SpringerGoogle Scholar
  8. 7.
    Pizlo Z, Li Y, Sawada T, Steinman RM (2014) Making a machine that sees like us. Oxford University PressGoogle Scholar
  9. 8.
    Leyton M (2014) Symmetry, causality, mind. MIT Press, Cambrige, MaGoogle Scholar
  10. 9.
    Sörgel U (ed) (1990) Radar remote sensing of urban areas. SpringerGoogle Scholar
  11. 10.
    Funk C, Lee S, Oswald MR, Tsokas S, Shen W, Cohen A, Dickinson S, Liu Y (2017) ICCV challenge: detecting symmetry in the wild. In: ICCV 2017, workshopsGoogle Scholar
  12. 11.
    Hebel M (2018) Shiftn – automatic correction of converging lines. Accessed Aug 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fraunhofer IOSBEttlingenGermany

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