• DocumentCode
    801416
  • Title

    Morphological pyramids with alternating sequential filters

  • Author

    Morales, Aldo ; Acharya, Raj ; Ko, Sung-Jea

  • Author_Institution
    Coll. of Eng., Pennsylvania State Univ., DuBois, PA, USA
  • Volume
    4
  • Issue
    7
  • fYear
    1995
  • fDate
    7/1/1995 12:00:00 AM
  • Firstpage
    965
  • Lastpage
    977
  • Abstract
    The aim of this paper is to find a relationship between alternating sequential filters (ASF) and the morphological sampling theorem (MST) developed by Haralick et al. (1987). The motivation behind this approach is to take advantage of the computational efficiency offered by the MST to implement morphological operations. First, we show alternative proofs for opening and closing in the sampled and unsampled domain using the basis functions. These proofs are important because they show that it possible to obtain any level of a morphological pyramid in one step rather than the traditional two-step procedure. This decomposition is then used to show the relationship of the open-closing in the sampled and unsampled domain. An upper and a lower bound, for the above relationships, are presented. Under certain circumstances, an equivalence is shown for open-closing between the sampled and the unsampled domain. An extension to more complicated algorithms using a union of openings and an intersection of closings is also proposed. Using the Hausdorff metric, it is shown that a morphologically reconstructed image cannot have a better accuracy than twice the radius of the reconstruction structuring element. Binary and gray scale examples are presented
  • Keywords
    digital filters; filtering theory; image reconstruction; image sampling; mathematical morphology; set theory; Hausdorff metric; algorithms; alternating sequential filters; basis functions; binary images; computational efficiency; decomposition; equivalence; gray scale images; intersection of closings; lower bound; morphological operations; morphological sampling theorem; morphologically reconstructed image; open-closing; reconstruction structuring element; sampled domain; union of openings; unsampled domain; upper bound; Computational efficiency; Computer vision; Filtering; Filters; Helium; Image reconstruction; Image sampling; Morphological operations; Robustness; Sampling methods;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7149
  • Type

    jour

  • DOI
    10.1109/83.392337
  • Filename
    392337