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Geometric Preservation of 2D Digital Objects Under Rigid Motions

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Abstract

Rigid motions (i.e. transformations based on translations and rotations) are simple, yet important, transformations in image processing. In \(\mathbb {R}^n\), they are both topology and geometry preserving. Unfortunately, these properties are generally lost in \(\mathbb {Z}^n\). In particular, when applying a rigid motion on a digital object, one generally alters its structure but also the global shape of its boundary. These alterations are mainly caused by digitization during the transformation process. In this specific context, some solutions for the handling of topological issues were proposed in \(\mathbb {Z}^2\). In this article, we also focus on geometric issues in \(\mathbb {Z}^2\). Indeed, we propose a rigid motion scheme that preserves geometry and topology properties of the transformed digital object: a connected object will remain connected, and some geometric properties (e.g. convexity, area and perimeter) will be preserved. To reach that goal, our main contributions are twofold. First, from an algorithmic point of view, our scheme relies on (1) a polygonization of the digital object, (2) the transformation of the intermediate piecewise affine object of \(\mathbb {R}^2\) and (3) a digitization step for recovering a result within \(\mathbb {Z}^2\). The intermediate modeling of a digital object of \(\mathbb {Z}^2\) as a piecewise affine object of \(\mathbb {R}^2\) allows us to avoid the geometric alterations generally induced by standard pointwise rigid motions. However, the final digitization of the polygon back to \(\mathbb {Z}^2\) has to be carried out cautiously. In particular, our second, theoretical contribution is a notion of quasi-regularity that provides sufficient conditions to be fulfilled by a continuous object for guaranteeing both topology and geometry preservation during its digitization.

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Notes

  1. This definition of r-regularity can be equivalently expressed as the invariance of \({ X }\) with respect to both opening and closing by a structuring element defined as a close ball of radius r. This mathematical morphology analogy will be given in Sect. 3.

  2. The boundary of \(\mathsf {X}\) is defined here as the boundary of the continuous object obtained as the union of the closed Voronoi cells associated to the points of \(\mathsf {X}\), in \(\mathbb {R}{}^2\).

  3. Kim introduced in [25] the definition of cellular convexity and proved the equivalence to the one using the convex hull [25, Lemma 10]. In [26], Eckhardt reformulated and renamed this notion H-convexity.

  4. http://ipol-geometry.loria.fr/~phuc/ipol_demo/RigidMotion2D.

References

  1. Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  2. Faisan, S., Passat, N., Noblet, V., Chabrier, R., Meyer, C.: Topology-preserving warping of binary images according to one-to-one mappings. IEEE Trans. Image Process. 20(8), 2135–2145 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  3. Ngo, P., Kenmochi, Y., Passat, N., Talbot, H.: Combinatorial structure of rigid transformations in 2D digital images. Comput. Vis. Image Underst. 117(4), 393–408 (2013)

    Article  MATH  Google Scholar 

  4. Pluta, K., Romon, P., Kenmochi, Y., Passat, N.: Bijective digitized rigid motions on subsets of the plane. J. Math. Imaging Vis. 59(1), 84–105 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  5. Ngo, P., Kenmochi, Y., Passat, N., Talbot, H.: Topology-preserving conditions for 2D digital images under rigid transformations. J. Math. Imaging Vis. 49(2), 418–433 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  6. Ngo, P., Passat, N., Kenmochi, Y., Talbot, H.: Topology-preserving rigid transformation of 2D digital images. IEEE Trans. Image Process. 23(2), 885–897 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  7. Pavlidis, T.: Algorithms for Graphics and Image Processing. Springer, Berlin (1982)

    Book  MATH  Google Scholar 

  8. Ngo, P., Kenmochi, Y., Debled-Rennesson, I., Passat, N.: Convexity-preserving rigid motions of 2D digital objects. In: Discrete Geometry for Computer Imagery, Vol. 1568, pp. 69–81 (2017)

  9. Klette, R., Rosenfeld, A.: Digital Geometry: Geometric Methods for Digital Picture Analysis. Elsevier, Amsterdam (2004)

    MATH  Google Scholar 

  10. Mazo, L., Passat, N., Couprie, M., Ronse, C.: Digital imaging: a unified topological framework. J. Math. Imaging Vis. 44(1), 19–37 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. Yung Kong, T., Rosenfeld, A.: Digital topology: introduction and survey. Comput. Vis. Gr. Image Process. 48(3), 357–393 (1989)

    Article  Google Scholar 

  12. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press Inc, Orlando (1983)

    Google Scholar 

  13. Latecki, L.J., Conrad, C., Gross, A.: Preserving topology by a digitization process. J. Math. Imaging Vis. 8(2), 131–159 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  14. Stelldinger, P., Köthe, U.: Towards a general sampling theory for shape preservation. Image Vis. Comput. 23(2), 237–248 (2005)

    Article  MATH  Google Scholar 

  15. Rosenfeld, A.: Adjacency in digital pictures. Inf. Control 26(1), 24–33 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  16. Rosenfeld, A., Kong, T.Y., Nakamura, A.: Topology-preserving deformations of two-valued digital pictures. Gr. Models Image Process. 60(1), 24–34 (1998)

    Article  Google Scholar 

  17. Boutry, N., Géraud, T., Najman, L.: A tutorial on well-composedness. J. Math. Imaging Vis. 60(3), 443–478 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  18. Maunder, C.R.F.: Algebraic Topology. Dover, New York (1996)

    MATH  Google Scholar 

  19. Rosenfeld, A.: Digital topology. Am. Math. Mon. 86(8), 621–630 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  20. Latecki, L.J., Eckhardt, U., Rosenfeld, A.: Well-composed sets. Comput. Vis. Image Underst. 61(1), 70–83 (1995)

    Article  Google Scholar 

  21. Heijmans, H.J.A.M., Ronse, C.: The algebraic basis of mathematical morphology. I Dilations and erosions. CVGIP Image Underst. 50(3), 245–295 (1990)

    MATH  Google Scholar 

  22. Ronse, C., Heijmans, H.J.A.M.: The algebraic basis of mathematical morphology: II. Openings and closings. CVGIP Image Underst. 54(1), 74–97 (1991)

    Article  MATH  Google Scholar 

  23. Minsky, M., Papert, S.: Perceptrons: An Introduction to Computational Geometry. MIT Press, Reading, MA (1969)

    MATH  Google Scholar 

  24. Sklansky, J.: Recognition of convex blobs. Pattern Recognit. 2(1), 3–10 (1970)

    Article  Google Scholar 

  25. Kim, C.E.: On the cellular convexity of complexes. IEEE Trans. Pattern Anal. Mach. Intell. 3(6), 617–625 (1981)

    Article  Google Scholar 

  26. Eckhardt, U.: Digital lines and digital convexity. In: Digital and Image Geometry: Advanced Lectures, pp. 209–228 (2001)

  27. Kim, C.E., Rosenfeld, A.: Digital straight lines and convexity of digital regions. IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 149–153 (1982)

    Article  MATH  Google Scholar 

  28. Cristescu, G., Lupsa, L.: Non-Connected Convexities and Applications. Kluwer Academic Publishers, Dordrecht (2002)

    Book  MATH  Google Scholar 

  29. Debled-Rennesson, I., Rémy, J.-L., Rouyer-Degli, J.: Detection of the discrete convexity of polyominoes. Discret. Appl. Math. 125(1), 115–133 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  30. Debled-Rennesson, I., Reveillès, J.: A linear algorithm for segmentation of digital curves. Int. J. Pattern Recognit. Artif. Intell. 9(4), 635–662 (1995)

    Article  Google Scholar 

  31. Feschet, F., Tougne, L.: Optimal time computation of the tangent of a discrete curve: application to the curvature. In: Discrete Geometry for Computer Imagery, Vol. 1568, pp. 31–40 (1999)

  32. Brlek, S., Lachaud, J., Provençal, X., Reutenauer, C.: Lyndon + Christoffel = digitally convex. Pattern Recognit. 42(10), 2239–2246 (2009)

    Article  MATH  Google Scholar 

  33. Duval, J.: Factorizing words over an ordered alphabet. J. Algorithms 4(4), 363–381 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  34. Melkman, A.A.: On-line construction of the convex hull of a simple polyline. Inf. Process. Lett. 25(1), 11–12 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  35. Anglin, W.S.: Using Pythagorean triangles to approximate angles. Am. Math. Mon. 95(6), 540–541 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  36. Sivignon, I., Breton, R., Dupont, F., Andres, E.: Discrete analytical curve reconstruction without patches. Image Vis. Comput. 23(2), 191–202 (2005)

    Article  Google Scholar 

  37. Dexet, M., Coeurjolly, D., Andres, E.: Invertible polygonalization of 3D planar digital curves and application to volume data reconstruction. In: International Symposium on Visual Computing, Vol. 4292, pp. 514–523 (2006)

  38. Vittone, J., Chassery, J.-M.: Recognition of digital naive planes and polyhedrization. In: Discrete Geometry for Computer Imagery, Vol. 1953, pp. 296–307 (2000)

  39. Feschet, F., Tougne, L.: On the min DSS problem of closed discrete curves. Discret. Appl. Math. 151(1–3), 138–153 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  40. Dörksen-Reiter, H., Debled-Rennesson, I.: Convex and concave parts of digital curves. In: Geometric Properties for Incomplete Data, pp. 145–159 (2006)

  41. Dörksen-Reiter, H., Debled-Rennesson, I.: A linear algorithm for polygonal representations of digital sets. In: International Workshop on Combinatorial Image Analysis, Vol. 4040, pp. 307–319 (2006)

  42. Roussillon, T., Sivignon, I.: Faithful polygonal representation of the convex and concave parts of a digital curve. Pattern Recognit. 44(10–11), 2693–2700 (2011)

    Article  MATH  Google Scholar 

  43. Nguyen, T.P., Debled-Rennesson, I.: A discrete geometry approach for dominant point detection. Pattern Recognit. 44(1), 32–44 (2011)

    Article  MATH  Google Scholar 

  44. Ngo, P., Nasser, H., Debled-Rennesson, I.: Efficient dominant point detection based on discrete curve structure. In: International Workshop on Combinatorial Image Analysis, Vol. 9448, pp. 143–156 (2015)

  45. Gérard, Y., Provot, L., Feschet, F.: Introduction to digital level layers. In: Discrete Geometry for Computer Imagery, Vol. 6607, pp. 83–94 (2011)

  46. Sivignon, I.: A near-linear time guaranteed algorithm for digital curve simplification under the Fréchet distance. In: Discrete Geometry for Computer Imagery, Vol. 6607, pp. 333–345 (2011)

  47. Fréchet, M.: Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Mathematico di Palermo 22, 1–74 (1906)

    Article  MATH  Google Scholar 

  48. Pick, G.: Geometrisches zur Zahlenlehre, Sitzungsberichte des Deutschen Naturwissenschaftlich-Medicinischen Vereines für Böhmen “Lotos” in Prag, 19, 311–319 (1899)

  49. Klette, R., Ẑunić, J.: Multigrid convergence of calculated features in image analysis. J. Math. Imaging Vis. 13, 173–191 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  50. Kovalevsky, V., Fuchs, S.: Theoretical and experimental analysis of the accuracy of perimeter estimates. In: Förster, W., Ruwiedel, S. (eds.) Robust Computer Vision, pp. 218–242 (1992)

  51. Coeurjolly, D., Lachaud, J.-O., Roussillon, T.: Multigrid convergence of discrete geometric estimators. In Brimkov, V.E., Barneva, R.P. (eds.) Digital Geometry Algorithms, Lecture Notes in Computational Vision and Biomechanics, vol. 2 pp. 395–424 (2012)

  52. Lachaud, J.-O., Thibert, B.: Properties of Gauss digitized shapes and digital surface integration. J. Math. Imaging Vis. 54(2), 162–180 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  53. DGtal: Digital geometry tools and algorithms library. http://libdgtal.org

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Acknowledgements

We would like to thank the anonymous referees for their valuable comments. In particular, we are especially grateful to referee 3, who pointed out an improved definition of quasi-r-regularity.

This work was partly funded by the French Agence Nationale de la Recherche, grant agreement ANR-15-CE40-0006 (CoMeDiC, https://lama.univ-savoie.fr/comedic), by the French Programmed’Investissements d’Avenir (LabEx Bézout, ANR-10-LABX-58) and by a mobility grant from the French Groupe de Recherche IGRV (CNRS).

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Ngo, P., Passat, N., Kenmochi, Y. et al. Geometric Preservation of 2D Digital Objects Under Rigid Motions. J Math Imaging Vis 61, 204–223 (2019). https://doi.org/10.1007/s10851-018-0842-9

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