The Medial Axis of Any Closed Bounded Set Is Lipschitz Stable with Respect to the Hausdorff Distance Under Ambient Diffeomorphisms

Authors Hana Dal Poz Kouřimská , André Lieutier, Mathijs Wintraecken



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Author Details

Hana Dal Poz Kouřimská
  • IST Austria, Klosterneuburg, Austria
André Lieutier
  • Aix-en-Provence, France
Mathijs Wintraecken
  • Inria Sophia Antipolis, Université Côte d'Azur, Sophia Antipolis, France

Acknowledgements

We are greatly indebted to Fred Chazal for sharing his insights. We further thank Erin Chambers, Christopher Fillmore, and Elizabeth Stephenson for early discussions and all members of the Edelsbrunner group (Institute of Science and Technology Austria) and the Datashape team (Inria) for the atmosphere in which this research was conducted.

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Hana Dal Poz Kouřimská, André Lieutier, and Mathijs Wintraecken. The Medial Axis of Any Closed Bounded Set Is Lipschitz Stable with Respect to the Hausdorff Distance Under Ambient Diffeomorphisms. In 40th International Symposium on Computational Geometry (SoCG 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 293, pp. 69:1-69:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.SoCG.2024.69

Abstract

We prove that the medial axis of closed sets is Hausdorff stable in the following sense: Let 𝒮 ⊆ ℝ^d be a fixed closed set that contains a bounding sphere. That is, the bounding sphere is part of the set 𝒮. Consider the space of C^{1,1} diffeomorphisms of ℝ^d to itself, which keep the bounding sphere invariant. The map from this space of diffeomorphisms (endowed with a Banach norm) to the space of closed subsets of ℝ^d (endowed with the Hausdorff distance), mapping a diffeomorphism F to the closure of the medial axis of F(𝒮), is Lipschitz. This extends a previous stability result of Chazal and Soufflet on the stability of the medial axis of C² manifolds under C² ambient diffeomorphisms.

Subject Classification

ACM Subject Classification
  • Theory of computation → Computational geometry
Keywords
  • Medial axis
  • Hausdorff distance
  • Lipschitz continuity

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References

  1. Eddie Aamari and Alexander Knop. Statistical query complexity of manifold estimation. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2021, pages 116-122, New York, NY, USA, 2021. Association for Computing Machinery. URL: https://doi.org/10.1145/3406325.3451135.
  2. Eddie Aamari and Clément Levrard. Stability and minimax optimality of tangential Delaunay complexes for manifold reconstruction. Discrete & Computational Geometry, 59:923-971, 2018. Google Scholar
  3. N. Amenta and M. Bern. Surface reconstruction by Voronoi filtering. Discrete & Computational Geometry, 22(4):481-504, December 1999. URL: https://doi.org/10.1007/PL00009475.
  4. Nina Amenta, Sunghee Choi, and Ravi Krishna Kolluri. The power crust. In Proceedings of the sixth ACM symposium on Solid modeling and applications, pages 249-266, 2001. Google Scholar
  5. Dominique Attali, Jean-Daniel Boissonnat, and Herbert Edelsbrunner. Stability and computation of medial axes - a state-of-the-art report. In Torsten Möller, Bernd Hamann, and Robert D. Russell, editors, Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, pages 109-125, Berlin, Heidelberg, 2009. Springer Berlin Heidelberg. URL: https://doi.org/10.1007/b106657_6.
  6. Dominique Attali and Annick Montanvert. Computing and simplifying 2d and 3d continuous skeletons. Computer vision and image understanding, 67(3):261-273, 1997. Google Scholar
  7. Matthias Bartelmann. Gravitational lensing. Classical and Quantum Gravity, 27(23):233001, November 2010. URL: https://doi.org/10.1088/0264-9381/27/23/233001.
  8. M. Berger. A Panoramic View of Riemannian Geometry. Springer Verlag, Berlin, Heidelberg, 2003. URL: https://doi.org/10.1007/978-3-642-18245-7.
  9. W. Blaschke. Kreis und Kugel. Verlag von Veit und Comp., Leipzig, 1916. Google Scholar
  10. Harry Blum. A Transformation for Extracting New Descriptors of Shape. In Weiant Wathen-Dunn, editor, Models for the Perception of Speech and Visual Form, pages 362-380. MIT Press, Cambridge, 1967. Google Scholar
  11. Erin Chambers, Ellen Gasparovic, and Kathryn Leonard. Medial fragments for segmentation of articulating objects in images. Research in Shape Analysis: WiSH2, Sirince, Turkey, June 2016, pages 1-15, 2018. Google Scholar
  12. F. Chazal, D. Cohen-Steiner, and A. Lieutier. A sampling theory for compact sets in Euclidean space. Discrete and Computational Geometry, 41(3):461-479, 2009. Google Scholar
  13. F. Chazal and A. Lieutier. The λ-medial axis. Graphical Models, 67(4):304-331, 2005. Google Scholar
  14. F. Chazal and R. Soufflet. Stability and finiteness properties of medial axis and skeleton. Journal of Dynamical and Control Systems, 10(2):149-170, 2004. URL: https://doi.org/10.1023/B:JODS.0000024119.38784.ff.
  15. Kim Coble, Kevin McLin, and Lynn Cominsky. Big Ideas in Cosmology. Libretexts Physics, UC Davis (online), 2020. Google Scholar
  16. James Damon. Geometry and Medial Structure, pages 69-123. Springer Netherlands, Dordrecht, 2008. URL: https://doi.org/10.1007/978-1-4020-8658-8_3.
  17. James Damon. Rigidity properties of the blum medial axis. Journal of Mathematical Imaging and Vision, 63(1):120-129, 2021. Google Scholar
  18. James Damon and Ellen Gasparovic. Medial/skeletal linking structures for multi-region configurations, volume 250 of Memoirs of the American Mathematical Society. American Mathematical Society, Providence, Rhode Island, 2017. Google Scholar
  19. Ilke Demir, Camilla Hahn, Kathryn Leonard, Geraldine Morin, Dana Rahbani, Athina Panotopoulou, Amelie Fondevilla, Elena Balashova, Bastien Durix, and Adam Kortylewski. SkelNetOn 2019: Dataset and challenge on deep learning for geometric shape understanding. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1143-1151, 2019. URL: https://doi.org/10.1109/CVPRW.2019.00149.
  20. Paul Erdős. Some remarks on the measurability of certain sets. Bulletin of the American Mathematical Society, 51(10):728-731, 1945. Google Scholar
  21. Paul Erdös. On the Hausdorff dimension of some sets in Euclidean space. Bulletin of the American Mathematical Society, 52(2):107-109, 1946. URL: https://doi.org/bams/1183507696.
  22. H. Federer. Curvature measures. Transactions of the America mathematical Society, 93:418-491, 1959. Google Scholar
  23. Charles Fefferman, Sergei Ivanov, Yaroslav Kurylev, Matti Lassas, and Hariharan Narayanan. Fitting a putative manifold to noisy data. In Conference On Learning Theory, pages 688-720. PMLR, 2018. Google Scholar
  24. Charles Fefferman, Sergei Ivanov, Matti Lassas, and Hariharan Narayanan. Fitting a manifold of large reach to noisy data. arXiv preprint arXiv:1910.05084, 2019. Google Scholar
  25. Charles Fefferman, Sergei Ivanov, Matti Lassas, and Hariharan Narayanan. Reconstruction of a Riemannian manifold from noisy intrinsic distances. SIAM Journal on Mathematics of Data Science, 2(3):770-808, 2020. Google Scholar
  26. J. D. Fernie. The period-luminosity relation: A historical review. Publications of the Astronomical Society of the Pacific, 81(483):707, December 1969. URL: https://doi.org/10.1086/128847.
  27. Ellen Gasparovic. The Blum medial linking structure for multi-region analysis. PhD thesis, The University of North Carolina at Chapel Hill, 2012. Google Scholar
  28. Seng-Beng Ho and Charles R Dyer. Shape smoothing using medial axis properties. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(4):512-520, 1986. URL: https://doi.org/10.1109/TPAMI.1986.4767815.
  29. Jianwei Hu, Bin Wang, Lihui Qian, Yiling Pan, Xiaohu Guo, Lingjie Liu, and Wenping Wang. MAT-Net: Medial axis transform network for 3D object recognition. In International Joint Conference on Artificial Intelligence, pages 774-781, 2019. URL: https://doi.org/10.24963/ijcai.2019/109.
  30. Chia-Chun Hung, Eric T Carlson, and Charles E Connor. Medial axis shape coding in macaque inferotemporal cortex. Neuron, 74(6):1099-1113, 2012. Google Scholar
  31. Vitali Kapovitch and Alexander Lytchak. Remarks on manifolds with two-sided curvature bounds. Analysis and Geometry in Metric Spaces, 9(1):53-64, 2021. Google Scholar
  32. Hana Dal Poz Kouřimská, André Lieutier, and Mathijs Wintraecken. The medial axis of any closed bounded set is lipschitz stable with respect to the hausdorff distance under ambient diffeomorphisms, 2023. URL: https://doi.org/10.48550/arXiv.2212.01118.
  33. Jean-Claude Latombe. Robot motion planning, volume 124 of The Springer International Series in Engineering and Computer Science. Springer, New York, 2012. URL: https://doi.org/10.1007/978-1-4615-4022-9.
  34. Mark D Lescroart and Irving Biederman. Cortical representation of medial axis structure. Cerebral cortex, 23(3):629-637, 2013. Google Scholar
  35. André Lieutier. Any open bounded subset of ℝⁿ has the same homotopy type as its medial axis. Computer-Aided Design, 36(11):1029-1046, 2004. Solid Modeling Theory and Applications. URL: https://doi.org/10.1016/j.cad.2004.01.011.
  36. André Lieutier and Mathijs Wintraecken. Hausdorff and gromov-hausdorff stable subsets of the medial axis. Proceedings of the 55th ACM Symposium on Theory of Computing (STOC 2023), 2023. URL: https://arxiv.org/abs/2303.04014.
  37. John N Mather. Distance from a submanifold in Euclidean space. In Proceedings of symposia in pure mathematics, volume 40, pages 199-216. American Mathematical Society, 1983. Google Scholar
  38. Phillip James Edwin Peebles. Principles of physical cosmology, volume 27. Princeton university press, Princeton, 1993. Google Scholar
  39. Morteza Rezanejad, Gabriel Downs, John Wilder, Dirk B Walther, Allan Jepson, Sven Dickinson, and Kaleem Siddiqi. Scene categorization from contours: Medial axis based salience measures. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4116-4124, 2019. Google Scholar
  40. Punam K Saha, Gunilla Borgefors, and Gabriella Sanniti di Baja. A survey on skeletonization algorithms and their applications. Pattern recognition letters, 76:3-12, 2016. Google Scholar
  41. Doron Shaked and Alfred M. Bruckstein. Pruning medial axes. Computer Vision and Image Understanding, 69(2):156-169, 1998. URL: https://doi.org/10.1006/cviu.1997.0598.
  42. Barak Sober and David Levin. Manifold approximation by moving least-squares projection (MMLS). Constructive Approximation, 52(3):433-478, 2020. Google Scholar
  43. Andrea Tagliasacchi, Thomas Delame, Michela Spagnuolo, Nina Amenta, and Alexandru Telea. 3d skeletons: A state-of-the-art report. In Computer Graphics Forum, volume 35, pages 573-597. Wiley Online Library, 2016. Google Scholar
  44. Zhongwei Tang, Rafael Grompone Von Gioi, Pascal Monasse, and Jean-Michel Morel. A precision analysis of camera distortion models. IEEE Transactions on Image Processing, 26(6):2694-2704, 2017. Google Scholar
  45. R. Thom. Sur le cut-locus d'une variété plongée. Journal of Differential Geometry, 6(4):577-586, 1972. URL: https://doi.org/10.4310/jdg/1214430644.
  46. Nhon H Trinh and Benjamin B Kimia. Skeleton search: Category-specific object recognition and segmentation using a skeletal shape model. International Journal of Computer Vision, 94:215-240, 2011. Google Scholar
  47. Martijn van Manen. Maxwell strata and caustics. In Jean-Paul Brasselet, James Damon, Lê Dũng Tráng, and Mutsuo Oka, editors, Singularities in Geometry and Topology, Proceedings of the Trieste Singularity Summer School and Workshop, pages 787-824. World Scientific, ICTP, Trieste, Italy, 2007. Google Scholar
  48. C. T. C. Wall. Geometric properties of generic differentiable manifolds. In Jacob Palis and Manfredo do Carmo, editors, Geometry and Topology, pages 707-774, Berlin, Heidelberg, 1977. Springer Berlin Heidelberg. Google Scholar
  49. Franz-Erich Wolter. Cut locus and medial axis in global shape interrogation and representation. MIT Department of Ocean Engineerring Design Laboratory Memorandum 92-2, 1993. Google Scholar
  50. Yajie Yan, Kyle Sykes, Erin Chambers, David Letscher, and Tao Ju. Erosion thickness on medial axes of 3d shapes. ACM Transactions on Graphics, 35(4):38:1-38:12, July 2016. URL: https://doi.org/10.1145/2897824.2925938.