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Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes

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Abstract

In this article, a new method for segmentation and restoration of images on two-dimensional surfaces is given. Active contour models for image segmentation are extended to images on surfaces. The evolving curves on the surfaces are mathematically described using a parametric approach. For image restoration, a diffusion equation with Neumann boundary conditions is solved in a postprocessing step in the individual regions. Numerical schemes are presented which allow to efficiently compute segmentations and denoised versions of images on surfaces. Also topology changes of the evolving curves are detected and performed using a fast sub-routine. Finally, several experiments are presented where the developed methods are applied on different artificial and real images defined on different surfaces.

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Notes

  1. https://graphics.stanford.edu/data/3Dscanrep/.

    Fig. 2
    figure 2

    Illustration of how triangles are assigned to a region. 1st sub-figure image and initial curve. 2nd sub-figure small band after \(n_0=4\) steps. 3rd sub-figure regions colored with mean brightness value after assignment of all triangles. The surface data are from the Stanford Computer Graphics Laboratory, cf. [31]

  2. http://faces.cs.unibas.ch/bfm/main.php?nav=1-0&id=basel_face_model.

  3. http://neo.sci.gsfc.nasa.gov/

  4. Imagery by Jesse Allen, NASA Earth Observatory, based on FLASHFlux data. FLASHFlux data are produced using CERES observations convolved with MODIS measurements from both the Terra and Aqua satellite. Data provided by the FLASHFlux team, NASA Langley Research Center.

References

  1. Balažovjech, M., Mikula, K., Petrášová, M., Urbán, J.: Lagrangean method with topological changes for numerical modelling of forest fire propagation. In: Proceedings of ALGORITMY 2012, 19th Conference on Scientific Computing, pp. 42–52. Vysoké Tatry, Podbansk’v, Slovakia (2012)

  2. Barrett, J.W., Garcke, H., Nürnberg, R.: A parametric finite element method for fourth order geometric evolution equations. J. Comput. Phys. 222(1), 441–467 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  3. Barrett, J.W., Garcke, H., Nürnberg, R.: On the variational approximation of combined second and fourth order geometric evolution equations. SIAM J. Sci. Comput. 29(3), 1006–1041 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  4. Barrett, J.W., Garcke, H., Nürnberg, R.: Numerical approximation of gradient flows for closed curves in \({\mathbb{R}}^d\). IMA J. Numer. Anal. 30(1), 4–60 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Benninghoff, H., Garcke, H.: Efficient image segmentation and restoration using parametric curve evolution with junctions and topology changes. SIAM J. Imaging Sci. 7(3), 1451–1483 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bertalmio, M., Cheng, L.T., Osher, S., Sapiro, G.: Variational problems and partial differential equations on implicit surfaces: the framework and examples in image processing and pattern formation. J. Comput. Phys. 174(2), 759–780 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  7. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vision 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  8. Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66, 1632–1648 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chan, T.F., Sandberg, B.Y., Vese, L.A.: Active contours without edges for vector-valued images. J. Vis. Commun. Image R. 11(2), 130–141 (2000)

    Article  Google Scholar 

  10. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  11. Cheng, L.T., Burchard, P., Merriman, B., Osher, S.: Motion of curves constrained on surfaces using a level-set approach. J. Comput. Phys. 175(2), 604–644 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  12. Čunderlík, R., Mikula, K., Tunega, M.: Nonlinear diffusion filtering of data on the Earth’s surface. J. Geod. 87(2), 143–160 (2013)

    Article  Google Scholar 

  13. Davis, T.: Algorithm 832: UMFPACK, an unsymmetric-pattern multifrontal method. ACM Trans. Math. Softw. 30(2), 196–199 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  14. Dziuk, G., Elliott, C.M.: Finite element methods for surface PDEs. Acta Numer. 22, 289–396 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Garcke, H., Wieland, S.: Surfactant spreading on thin viscous films: nonnegative solutions of a coupled degenerate system. SIAM J. Math. Anal. 37(6), 2025–2048 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

  17. Kimmel, R.: Intrinsic scale space for images on surfaces: the geodesic curvature flow. Graph. Model. Im. Proc. 59(5), 365–372 (1997)

    Article  Google Scholar 

  18. Krüger, M., Delmas, P., Gimelfarb, G.: Active contour based segmentation of 3D surfaces. In: Proceedings of the European Conference on Computer Vision, pp. 350–363. Marseille, France (2008)

  19. Lai, R., Chan, T.F.: A framework for intrinsic image processing on surfaces. Comput. Vis. Image Und. 115(12), 1647–1661 (2011)

    Article  Google Scholar 

  20. Lang, S.: Introduction to differentiable manifolds, 2nd edn. Universitext. Springer, Berlin (2002)

    MATH  Google Scholar 

  21. Lee, J.M.: Introduction to smooth manifolds, graduate texts in mathematics, vol. 218. Springer, Heidelberg (2002)

    Google Scholar 

  22. Mikula, K., Urbán, J.: New fast and stable Lagrangean method for image segmentation. In: Proceedings of the 5th International Congress on Image and Signal Processing (CISP 2012), pp. 834–842. Chongquing (2012)

  23. Mikula, K., Ševčovič, D.: Evolution of curves on a surface driven by the geodesic curvature and external force. Appl. Anal. 85(4), 345–362 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  24. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure. Appl. Math. 42, 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  25. NASA: NASA Earth Observations (2014). http://neo.sci.gsfc.nasa.gov/

  26. Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  27. Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3D face model for pose and illumination invariant face recognition. In: Proceedings of the 6th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) for Security. Safety and Monitoring in Smart Environments, pp. 296–301. Genova (2009)

  28. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  29. Spira, A., Kimmel, R.: Geometric curve flows on parametric manifolds. J. Comput. Phys. 223, 235–249 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  30. Tian, L., Macdonald, C.B., Ruuth, S.J.: Segmentation on surfaces with the closest point method. In: Proceedings of the 16th IEEE International Conference on Image Processing, pp. 3009–3012. Cairo (2009)

  31. Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: Proceedings of the 21st annual conference on Computer graphics and interactive techniques (SIGGRAPH ’94), pp. 311–318. ACM, New York (1994)

  32. Wu, C., Tai, X.C.: Augmented Lagrangian method dual methods, and split bregman iteration for ROF, vectorial TV, and high order models. SIAM J. Imaging Sci. 3(3), 300–339 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  33. Wu, C., Zhang, J., Duan, Y., Tai, X.C.: Augmented Lagrangian method for total variation based image restoration and segmentation over triangulated surfaces. J. Sci. Comput. 50(1), 145–166 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhou, L., Li, J.: Image segmentation on implicit surface based on Chan-Vese model. J. Theor. Appl. Inf. Technol. 48(1), 206–209 (2013)

    Google Scholar 

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Benninghoff, H., Garcke, H. Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes. J Math Imaging Vis 55, 105–124 (2016). https://doi.org/10.1007/s10851-015-0616-6

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