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Morphology for Color Images via Loewner Order for Matrix Fields

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7883))

Abstract

Mathematical morphology is a very successful branch of image processing with a history of more than four decades. Its fundamental operations are dilation and erosion, which are based on the notion of a maximum and a minimum with respect to an order. Many operators constructed from dilation and erosion are available for grey value images, and recently useful analogs of these processes for matrix-valued images have been introduced by taking advantage of the so-called Loewner order. There has been a number of approaches to morphology for vector-valued images, that is, colour images based on various orders, however, each with its merits and shortcomings. In this article we propose an approach to (elementary) morphology for colour images that relies on the existing order based morphology for matrix fields of symmetric 2×2-matrices. An RGB-image is embedded into a field of those 2×2-matrices by exploiting the geometrical properties of the order cone associated with the Loewner order. To this end a modification of the HSL-colour model and a relativistic addition of matrices is introduced.

The experiments performed with various morphological elementary operators on synthetic and real images demonstrate the capabilities and restrictions of the novel approach.

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References

  1. Agoston, M.K.: Computer Graphics and Geometric Modeling: Implementation and Algorithms. Springer, London (2005)

    Google Scholar 

  2. Aptoula, E., Lefèvre, S.: A comparative study on multivariate mathematical morphology. Pattern Recognition 40(11), 2914–2929 (2007)

    Article  MATH  Google Scholar 

  3. Barnett, V.: The ordering of multivariate data. Journal of the Statistical Society, A 139(3), 318–355 (1976)

    Article  Google Scholar 

  4. Borwein, J.M., Lewis, A.S.: Convex Analysis and Nonlinear Optimization. Springer, New York (2000)

    MATH  Google Scholar 

  5. Brox, T., Weickert, J.: A TV flow based local scale measure for texture discrimination. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 578–590. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Burgeth, B., Bruhn, A., Papenberg, N., Welk, M., Weickert, J.: Mathematical morphology for tensor data induced by the Loewner ordering in higher dimensions. Signal Processing 87(2), 277–290 (2007)

    Article  MATH  Google Scholar 

  7. Burgeth, B., Kleefeld, A.: Order based morphology for color images via matrix fields. In: Burgeth, B., Vilanova, A., Westin, C.-F. (eds.) Visualization and Processing of Tensor Fields and Higher Order Descriptors for Multi-Valued Data. Springer, Berlin (submitted)

    Google Scholar 

  8. Burgeth, B., Papenberg, N., Bruhn, A., Welk, M., Feddern, C., Weickert, J.: Mathematical morphology based on the loewner ordering for tensor data. In: Ronse, C., Najman, L., Decencière, E. (eds.) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol. 30, pp. 407–418. Springer, Dordrecht (2005)

    Chapter  Google Scholar 

  9. Burgeth, B., Welk, M., Feddern, C., Weickert, J.: Mathematical morphology on tensor data using the Loewner ordering. In: Weickert, J., Hagen, H. (eds.) Visualization and Processing of Tensor Fields, pp. 357–367. Springer, Berlin (2006)

    Chapter  Google Scholar 

  10. Comer, M.L., Delp, E.J.: Morphological operations for color image processing. Journal of Electronic Imaging 8(3), 279–289 (1999)

    Article  Google Scholar 

  11. Dougherty, E.R. (ed.): Anamorphoses and function lattices, Mathematical Morphology in Image Processing, pp. 483–523. Marcel Dekker, New York (1993)

    Google Scholar 

  12. Gaertner, B.: Smallest enclosing balls of points - fast and robust in C++, http://www.inf.ethz.ch/personal/gaertner/miniball.html (last visited July 03, 2012)

  13. Goutsias, J., Heijmans, H.J.A.M., Sivakumar, K.: Morphological operators for image sequences. Computer Vision and Image Understanding 62, 326–346 (1995)

    Article  Google Scholar 

  14. Heijmans, H.J.A.M.: Morphological Image Operators. Academic Press, Boston (1994)

    MATH  Google Scholar 

  15. Matheron, G.: Eléments pour une théorie des milieux poreux. Masson, Paris (1967)

    Google Scholar 

  16. Serra, J.: Echantillonnage et estimation des phénomènes de transition minier. PhD thesis, University of Nancy, France (1967)

    Google Scholar 

  17. Serra, J.: Image Analysis and Mathematical Morphology, vol. 1. Academic Press, London (1982)

    MATH  Google Scholar 

  18. Serra, J.: Image Analysis and Mathematical Morphology, vol. 2. Academic Press, London (1988)

    Google Scholar 

  19. Sexl, R.U., Urbantke, H.K.: Relativity, Groups, Particles: Special Relativity and Relativistic Symmetry in Field and Particle Physics. Springer, Wien (2001)

    Book  MATH  Google Scholar 

  20. Soille, P.: Morphological Image Analysis, 2nd edn. Springer, Berlin (2003)

    MATH  Google Scholar 

  21. Ungar, A.A.: Einstein’s special relativity: The hyperbolic geometric viewpoint. In: Conference on Mathematics, Physics and Philosophy on the Interpretations of Relativity, II, Budapest (2009)

    Google Scholar 

  22. Xu, S., Freund, R.M., Sun, J.: Solution Methodologies for the Smallest Enclosing Circle Problem. In: High Performance Computation for Engineered Systems (HPCES) (2003), http://hdl.handle.net/1721.1/4015

    Google Scholar 

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Burgeth, B., Kleefeld, A. (2013). Morphology for Color Images via Loewner Order for Matrix Fields. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2013. Lecture Notes in Computer Science, vol 7883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38294-9_21

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  • DOI: https://doi.org/10.1007/978-3-642-38294-9_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38293-2

  • Online ISBN: 978-3-642-38294-9

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