• DocumentCode
    2430944
  • Title

    Urban building detection by visual and geometrical features

  • Author

    Trinh, Hoang-Hon ; Kim, Dae-Nyeon ; Jo, Kang-Huyn

  • Author_Institution
    Univ. of Ulsan, Ulsan
  • fYear
    2007
  • fDate
    17-20 Oct. 2007
  • Firstpage
    1779
  • Lastpage
    1784
  • Abstract
    This paper describes an approach to detect the buildings in the urban environment. Visual and geometrical features of line segments are used to classify the building in the images. The buildings are also distinguished with other objects like sky, tree, bush and roads. Firstly, the line segments of building and non-building patterns are separated. The natural features are the contrast between two neighbored regions of segment, vanishing points, the appeared density, the vertical and horizontal alongside distributions. Those features are used to step-by-step reduce the segments of non-building pattern. The rests called the basic segments are grouped to create a mesh of skewed parallelograms. Each mesh represents a partial face of buildings. Finally, the faces or facets of building are detected by combining the neighbored partial faces. The building facet is refined again by its area. The proposed approach has been experimented for over 800 test images with the high rate of detection results.
  • Keywords
    building; image classification; image segmentation; mesh generation; building classification; building facet; geometrical features; image classification; line segments; nonbuilding pattern; skewed parallelogram mesh; urban building detection; visual features; Automatic control; Buildings; Face detection; Image databases; Image segmentation; Multilevel systems; Object detection; Personal communication networks; Roads; Testing; Building and non-building patterns; skewed parallelogram; vanishing points;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems, 2007. ICCAS '07. International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    978-89-950038-6-2
  • Electronic_ISBN
    978-89-950038-6-2
  • Type

    conf

  • DOI
    10.1109/ICCAS.2007.4406633
  • Filename
    4406633