• DocumentCode
    1551721
  • Title

    Directional morphological filtering

  • Author

    Soille, Pierre ; Talbot, Hugues

  • Author_Institution
    EC Joint Research Centre, Space Applications Inst., Ispra, Italy
  • Volume
    23
  • Issue
    11
  • fYear
    2001
  • fDate
    11/1/2001 12:00:00 AM
  • Firstpage
    1313
  • Lastpage
    1329
  • Abstract
    We show that a translation invariant implementation of min/max filters along a line segment of slope in the form of an irreducible fraction dy/dx can be achieved at the cost of 2+k min/max comparisons per image pixel, where k=max(|dx|,|dy|). Therefore, for a given slope, the computation time is constant and independent of the length of the line segment. We then present the notion of periodic moving histogram algorithm. This allows for a similar performance to be achieved in the more general case of rank filters and rank-based morphological filters. Applications to the filtering of thin nets and computation of both granulometries and orientation fields are detailed. Finally, two extensions are developed. The first deals with the decomposition of discrete disks and arbitrarily oriented discrete rectangles, while the second concerns min/max filters along gray tone periodic line segments
  • Keywords
    bibliographies; filtering theory; image processing; mathematical morphology; arbitrarily oriented discrete rectangles; computation time; directional filters; directional morphological filtering; discrete disks; discrete geometry; general case; granulometries; gray tone periodic line segments; image pixel; irreducible fraction; line segment; mathematical morphology; min/max comparisons; min/max filters; orientation fields; periodic moving histogram algorithm; radial decomposition; rank filters; rank-based morphological filters; thin nets; translation invariant implementation; Computational geometry; Costs; Filtering; Filters; Histograms; Image analysis; Image segmentation; Morphology; Pixel; Shape;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/34.969120
  • Filename
    969120