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Algebraic and PDE Approaches for Lattice Scale-Spaces with Global Constraints

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Abstract

This paper begins with analyzing the theoretical connections between levelings on lattices and scale-space erosions on reference semilattices. They both represent large classes of self-dual morphological operators that exhibit both local computation and global constraints. Such operators are useful in numerous image analysis and vision tasks including edge-preserving multiscale smoothing, image simplification, feature and object detection, segmentation, shape and motion analysis. Previous definitions and constructions of levelings were either discrete or continuous using a PDE. We bridge this gap by introducing generalized levelings based on triphase operators that switch among three phases, one of which is a global constraint. The triphase operators include as special cases useful classes of semilattice erosions. Algebraically, levelings are created as limits of iterated or multiscale triphase operators. The subclass of multiscale geodesic triphase operators obeys a semigroup, which we exploit to find PDEs that can generate geodesic levelings and continuous-scale semilattice erosions. We discuss theoretical aspects of these PDEs, propose discrete algorithms for their numerical solution which converge as iterations of triphase operators, and provide insights via image experiments.

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References

  • Alvarez, L., Guichard, F., Lions, P.L., and Morel, J.M. 1993. Axioms and fundamental equations of image processing. Archiv. Rat. Mech., 123(3):199–257.

    Google Scholar 

  • Brockett, R. and Maragos, P. 1994. Evolution equations for continuous-scale morphological filtering. IEEE Trans. Signal Process., 42:3377–3386.

    Google Scholar 

  • Heijmans, H.J.A.M. 1994. Morphological Image Operators. Academic Press: Boston.

    Google Scholar 

  • Heijmans, H.J.A.M. and Keshet, R. 2000. First steps towards a self-dual morphology. In Proc. Int'l Conf. Image Processing, Vancouver, Canada.

  • Heijmans, H.J.A.M. and Keshet (Kresch), R. 2001. Inf-semilattice approach to self-dual morphology. Report PNA-R0101, CWI, Amsterdam.

  • Keshet (Kresch), R. 2000. Mathematical morphology on complete semilattices and its applications to image processing. Fundamentae Informatica, 41:33–56.

    Google Scholar 

  • Maragos, P. 1989. Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. Machine Intellig., 11:701– 716.

    Google Scholar 

  • Maragos, P. and Meyer, F. 1999. Nonlinear PDEs and numerical algorithms for modeling levelings and reconstruction filters. In Scale-Space Theories in Computer Vision, Lecture Notes in Computer Science, Vol. 1682. Springer; pp. 363– 374.

    Google Scholar 

  • Matheron, G. 1975. Random Sets and Integral Geometry. Wiley; New York.

    Google Scholar 

  • Matheron, G. 1997. FrLes Nivellements. Technical Report, Centre de Morphologie Mathematique.

  • Meyer, F. 1998. The levelings. In Mathematical Morphology and Its Applications to Image and Signal Processing, H. Heijmans and J. Roerdink (Eds.), Kluwer Academic: Bostan, MA.

    Google Scholar 

  • Meyer, F. and Maragos, P. 2000. Nonlinear scale-space representation with morphological levelings. J. Visual Commun. & Image Representation, 11:245–265.

    Google Scholar 

  • Meyer, F. and Serra, J. 1989. Contrasts and activity lattice. Signal Processing, 16(4):303–317.

    Google Scholar 

  • Osher, S. and Rudin, L. I. 1990. Feature-oriented image enhancement using schock filters. SIAM J. Numer. Anal., 27(4):919– 940.

    Google Scholar 

  • Osher, S. and Sethian, J. 1988. Fronts propagating with curvaturedependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comp. Phys., 79:12–49.

    Google Scholar 

  • Salembier, P. and Serra, J. 1995. Flat zones filtering, conencted operators, and filters by reconstruction. IEEE Trans. Image Process., 4:1153–1160.

    Google Scholar 

  • Sapiro, G., Kimmel, R., Shaked, D., Kimia, B., and Bruckstein, A. 1993. Implementing continuous-scale morphology via curve evolution. Pattern Recognition, 26(9):1363–1372.

    Google Scholar 

  • Serra, J. 1982. Image Analysis and Mathematical Morphology. Academic Press: New York.

    Google Scholar 

  • Serra, J. (Ed.). 1988. Image Analysis and Mathematical Morphology. Vol. 2. Academic Press: New York.

    Google Scholar 

  • Serra, J. 2000. Connections for sets and functions. Fundamentae Informatica, 41:147–186.

    Google Scholar 

  • Vincent, L. 1993. Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Trans. Image Proces., 2(2):176–201.

    Google Scholar 

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Maragos, P. Algebraic and PDE Approaches for Lattice Scale-Spaces with Global Constraints. International Journal of Computer Vision 52, 121–137 (2003). https://doi.org/10.1023/A:1022999923439

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